We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

The atmospheric concentration of CO2 at the time of passing 1.5 °C or 2 °C is unknown due to uncertainties in climate sensitivity and the concentrations of other GHGs. Impacts studies must account for a wide range of concentrations to avoid either over-or underestimating changes in crop yields and land and marine biodiversity.

A new generation of an Earth system model now includes a number of land-surface processes directly relevant to analyzing potential impacts of climate change. This model, HadGEM2-ES, allows us to assess the impacts of climate change, multiple interactions, and feedbacks as the model is run. This paper discusses the results of century-scale HadGEM2-ES simulations from an impacts perspective - specifically, terrestrial ecosystems and water resources - for four different scenarios following the representative concentration pathways (RCPs), used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013, 2014). Over the 21st century, simulated changes in global and continental-scale terrestrial ecosystems due to climate change appear to be very similar in all 4 RCPs, even though the level of global warming by the end of the 21st century ranges from 2°C in the lowest scenario to 5.5° in the highest. A warming climate generally favours broadleaf trees over needleleaf, needleleaf trees over shrubs, and shrubs over herbaceous vegetation, resulting in a poleward shift of temperate and boreal forests and woody tundra in all scenarios. Although climate related changes are slightly larger in scenarios of greater warming, the largest differences between scenarios arise at regional scales as a consequence of different patterns of anthropogenic land cover change. In the model, the scenario with the lowest global warming results in the most extensive decline in tropical forest cover due to a large expansion of agriculture. Under all four RCPs, fire potential could increase across extensive land areas, particularly tropical and sub-tropical latitudes. River outflows are simulated to increase with higher levels of CO2 and global warming in all projections, with outflow increasing with mean temperature at the end of the 21st century at the global scale and in North America, Asia, and Africa. In South America, Europe, and Australia, the relationship with climate warming and CO2 rise is less clear, probably as a result of land cover change exerting a dominant effect in those regions.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) assessed a range of scenarios of future greenhouse-gas emissions without policies to specifically reduce emissions, and concluded that these would lead to an increase in global mean temperatures of between 1.6°C and 6.9°C by the end of the twenty-first century, relative to pre-industrial. While much political attention is focused on the potential for global warming of 2°C relative to pre-industrial, the AR4 projections clearly suggest that much greater levels of warming are possible by the end of the twenty-first century in the absence of mitigation. The centre of the range of AR4-projected global warming was approximately 4°C. The higher end of the projected warming was associated with the higher emissions scenarios and models, which included stronger carbon-cycle feedbacks. The highest emissions scenario considered in the AR4 (scenario A1FI) was not examined with complex general circulation models (GCMs) in the AR4, and similarly the uncertainties in climate-carbon-cycle feedbacks were not included in the main set of GCMs. Consequently, the projections of warming for A1FI and/or with different strengths of carbon-cycle feedbacks are often not included in a wider discussion of the AR4 conclusions. While it is still too early to say whether any particular scenario is being tracked by current emissions, A1FI is considered to be as plausible as other non-mitigation scenarios and cannot be ruled out. (A1FI is a part of the A1 family of scenarios, with 'FI' standing for 'fossil intensive'. This is sometimes erroneously written as A1F1, with number 1 instead of letter I.) This paper presents simulations of climate change with an ensemble of GCMs driven by the A1FI scenario, and also assesses the implications of carbon-cycle feedbacks for the climate-change projections. Using these GCM projections along with simple climate-model projections, including uncertainties in carbon-cycle feedbacks, and also comparing against other model projections from the IPCC, our best estimate is that the A1FI emissions scenario would lead to a warming of 4°C relative to pre-industrial during the 2070s. If carbon-cycle feedbacks are stronger, which appears less likely but still credible, then 4°C warming could be reached by the early 2060s in projections that are consistent with the IPCC's 'likely range'.

In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology. Plant stomata generally open less widely under increased carbon dioxide concentration, which reduces transpiration and thus leaves more water at the land surface. This driver of change in the climate system, which we term 'physiological forcing', has been detected in observational records of increasing average continental runoff over the twentieth century. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11 +/- 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources.

Glacier- and snowpack-derived meltwaters are threatened by climate change. Features such as rock glaciers (RGs) are climatically more resilient than glaciers and potentially contain hydrologically valuable ice volumes. However, while the distribution and hydrological significance of glaciers is well studied, RGs have received comparatively little attention. Here, we present the first near-global RG database (RGDB) through an analysis of current inventories and this contains >73,000 RGs. Using the RGDB, we identify key data-deficient regions as research priorities (e.g. Central Asia). We provide the first approximation of near-global RG water volume equivalent and this is 83.72 ± 16.74 Gt. Excluding the Antarctic and Subantarctic, Greenland, and regions lacking data, we estimate a near-global RG to glacier water volume equivalent ratio of 1:456. Significant RG water stores occur in arid and semi-arid regions (e.g. South Asia East, 1:57). These results represent a first-order approximation. Uncertainty in the water storage estimates includes errors within the RGDB, inherent flaws in the meta-analysis methodology, and RG thickness estimation. Here, only errors associated with the assumption of RG ice content are quantified and overall uncertainty is likely larger than that quantified. We suggest that RG water stores will become increasingly important under future climate warming.

We outline the principles of the natural capital approach to decision making and apply these to the contemporary challenge of very significantly expanding woodlands as contribution to attaining net zero emissions of greenhouse gases. Drawing on the case of the UK, we argue that a single focus upon carbon storage alone is likely to overlook the other ‘net zero plus’ benefits which woodlands can deliver. A review of the literature considers the wide variety of potential benefits which woodlands can provide, together with costs such as foregone alternative land uses. We argue that decision making must consider all of these potential benefits and costs for the right locations to be planted with the right trees. The paper closes by reviewing the decision support systems necessary to incorporate this information into policy and decision making. Read the free Plain Language Summary for this article on the Journal blog.

Land management and deforestation in tropical regions cause wildfires and forest degradation, leading to a loss of ecosystem services and global climate regulation. The objective of the study was to provide a comprehensive assessment of the spatial extent and patterns of burned areas in a new deforestation frontier in the Amazonas state. The methodology applied cross-referenced burned area data from 2003 to 2019 with climate, land cover, private properties and Protected Areas information and performed a series of statistical tests. The influence of the Multivariate ENSO Index (MEI) contributed to a decreasing rainfall anomalies trend and increasing temperature anomalies trend. This process intensified the dry season and increased the extent of annual natural vegetation affected by fires, reaching a peak of 681 km2 in 2019. The results showed that the increased deforestation trend occurred mostly in public lands, mainly after the new forest code, leading to an increase in fires from 66 to 84% in 2019. The methods developed here could identify fire extent, trends, and relationship with land cover change and climate, thus pointing to priority areas for preservation. The conclusion presented that policy decisions affecting the Amazon Forest must include estimates of fire risk and impact under current and projected future climates.

The increase in greenhouse gasses (GHG) anthropogenic emissions and deforestation over the last decades have led to many chemical and physical changes in the climate system, affecting the atmosphere's energy and water balance. A process that could be affected is the Amazonian moisture transport in the South American continent (including La Plata basin), which is crucial to the southeast Brazilian water regime. The focus of our research is on evaluating how local (i.e. Amazon deforestation) and global forcings (increase of atmospheric GHG concentration) may modify this moisture transport under climate change scenarios. We used two coupled land-atmosphere models forced by CMIP6 sea surface temperatures to simulate these processes for two scenarios: i) increase in carbon dioxide (CO2) — RCP8.5 atmospheric levels (00DEF), and ii) total Amazon deforestation simultaneous with atmospheric CO2 levels increased (100DEF). These scenarios were compared with a control simulation, set with a constant CO2 of 388 ppm and present-day Amazon Forest cover. The 30-year Specific Warming Level 2 (SWL2) index evaluated from the simulations is set to be reached 2 years earlier due to Amazon deforestation. A reduction in precipitation was observed in the Amazon basin (−3.1 mm·day−1) as well as in La Plata Basin (−0.5 mm·day−1) due to reductions in the Amazon evapotranspiration (−0.9 mm·day−1) through a stomatal conductance decrease (00DEF) and land cover change (100DEF). In addition, the income moisture transport decreased (22 %) in the northern La Plata basin in both scenarios and model experiments. Our results indicated a worse scenario than previously found in the region. Both Amazon and La Plata hydrological regimes are connected (moisture and energy transport), indicating that a large-scale Amazon deforestation will have additional climate, economic and social implications for South America.

Blue carbon has made significant contributions to climate change adaptation and mitigation while assisting in achieving co-benefits such as aquaculture development and coastal restoration, winning international recognition. Climate change mitigation and co-benefits from blue carbon ecosystems are highlighted in the recent Intergovernmental Panel on Climate Change Special Report on Ocean and Cryosphere in a Changing Climate. Its diverse nature has resulted in unprecedented collaboration across disciplines, with conservationists, academics, and politicians working together to achieve common goals such as climate change mitigation and adaptation, which need proper policy regulations, funding, and multi-prong and multi-dimensional strategies to deal with. An overview of blue carbon habitats such as seagrass beds, mangrove forests, and salt marshes, the critical role of blue carbon ecosystems in mitigating plastic/micro-plastic pollution, as well as the utilization of the above-mentioned blue carbon resources for biofuel production, are critically presented in this research. It also highlights the concerns about blue carbon habitats. Identifying and addressing these issues might help preserve and enhance the ocean’s ability to store carbon and combat climate change and mitigate plastic/micro-plastic pollution. Checking out their role in carbon sequestration and how they act as the major carbon sinks of the world are integral parts of this study. In light of the global frameworks for blue carbon and the inclusion of microalgae in blue carbon, blue carbon ecosystems must be protected and restored as part of carbon stock conservation efforts and the mitigation of plastic/micro-plastic pollution. When compared to the ecosystem services offered by terrestrial ecosystems, the ecosystem services provided by coastal ecosystems, such as the sequestration of carbon, the production of biofuels, and the remediation of pollution, among other things, are enormous. The primary purpose of this research is to bring awareness to the extensive range of beneficial effects that can be traced back to ecosystems found in coastal environments.

Afforestation and reforestation to meet 'Net Zero' emissions targets are considered a necessary policy by many countries. Their potential benefits are usually assessed through forest carbon and growth models. The implementation of vegetation demography gives scope to represent forest management and other size-dependent processes within land surface models (LSMs). In this paper, we evaluate the impact of including management within an LSM that represents demography, using both in-situ and reanalysis climate drivers at a mature, upland Sitka spruce plantation in Northumberland, UK. We compare historical simulations with fixed and variable CO2 concentrations, and with and without tree thinning implemented. Simulations are evaluated against the observed vegetation structure and carbon fluxes. Including thinning and the impact of increasing CO2 concentration ('CO2 fertilisation') gave more realistic estimates of stand-structure and physical characteristics. Historical CO2 fertilisation had a noticeable effect on the Gross Primary Productivity seasonal-diurnal cycle and contributed to approximately 7% higher stand biomass by 2018. The net effect of both processes resulted in a decrease of tree density and biomass, but an increase in tree height and leaf area index.

Peatlands of the central Congo Basin have accumulated carbon over millennia. They currently store some 29 billion tonnes of carbon in peat. However, our understanding of the controls on peat carbon accumulation and loss and the vulnerability of this stored carbon to climate change is in its infancy. Here we present a new model of tropical peatland development, DigiBog_Congo, that we use to simulate peat carbon accumulation and loss in a rain-fed interfluvial peatland that began forming ~20,000 calendar years Before Present (cal. yr BP, where 'present' is 1950 CE). Overall, the simulated age-depth curve is in good agreement with palaeoenvironmental reconstructions derived from a peat core at the same location as our model simulation. We find two key controls on long-term peat accumulation: water at the peat surface (surface wetness) and the very slow anoxic decay of recalcitrant material. Our main simulation shows that between the Late Glacial and early Holocene there were several multidecadal periods where net peat and carbon gain alternated with net loss. Later, a climatic dry phase beginning ~5200 cal. yr BP caused the peatland to become a long-term carbon source from ~3975 to 900 cal. yr BP. Peat as old as ~7000 cal. yr BP was decomposed before the peatland's surface became wetter again, suggesting that changes in rainfall alone were sufficient to cause a catastrophic loss of peat carbon lasting thousands of years. During this time, 6.4 m of the column of peat was lost, resulting in 57% of the simulated carbon stock being released. Our study provides an approach to understanding the future impact of climate change and potential land-use change on this vulnerable store of carbon.

Past and future trends in the frequency of high-danger fire weather conditions have been analysed for the UK. An analysis of satellite-derived burned area data from the last 18 years has identified the seasonal cycle with a peak in spring and a secondary peak in summer, a high level of interannual variability, and a lack of a significant trend despite some large events occurring in the last few years. These results were confirmed with a longer series of fire weather indices back to 1979. The Initial Spread Index (ISI) has been used for spring, as this reflects the moisture of fine fuel surface vegetation, whereas conditions conducive to summer wildfires are hot, dry weather reflected in the moisture of deeper organic layers which is encompassed in the Fire Weather Index (FWI). Future projections are assessed using an ensemble of regional climate models from the UK Climate Projections, combining variables to derive the fire weather indices. The results show a large increase in hazardous fire weather conditions in summer. At 2°C global warming relative to 1850-1900, the frequency of days with "very high"fire danger is projected to double compared to the recent historical period. This frequency increases by a factor of 5 at 4°C of global warming. Smaller increases are projected for spring, with a 150ĝ€¯% increase for England at 2°C of global warming and a doubling at 4°C. A particularly large projected increase for late summer and early autumn suggests a possible extension of the wildfire season, depending on fuel availability. These results suggest that wildfire can be considered an "emergent risk"for the UK, as past events have not had widespread major impacts, but this could change in future, with adaptation actions being required to manage the future risk. The large increase in risk between the 2 and 4°C levels of global warming highlights the importance of global efforts to keep warming below 2°C.

With the inclusion of demographic characteristics of the population living in vulnerable areas, a combination of empirical and climate models was used to project changes to climate and in hydro-geo-meteorological disasters in Brazil. This study investigated the effect of extreme rainfall changes and the risk of floods and landslides under 1.5, 2.0, and 4.0°C global warming levels (GWLs). Projections from a large ensemble of pre-CMIP6 models and different warming levels show a remarkable change in heavy precipitation. As a result, with increasing warming this enhances the risk of landslides and flash floods in the context of climate change. Comparisons of vulnerability and change in potential impacts of landslides and floods show that three regions, highly densely populated areas, are the most exposed to landslides and floods. The Southern and Southeastern of Brazil stand out, including metropolitan regions with high economic development and densely populated, which may be those where disasters can intensify both in terms of frequency and magnitude. The eastern portion of the Northeast is also signaled as one of the affected regions due to its high vulnerability and exposure since the present period, although the projections of future climate do not allow conclusive results regarding the intensification of extreme rainfall events in scenarios below 4°C. The main metropolitan regions and tourist resorts, and key infrastructure in Brazil are located in those regions. This study highlights the importance of environmental policies to protect human lives and minimize financial losses in the coming decades and reinforces the need for decision-making, monitoring, and early warning systems to better manage disasters as part of disaster risk reduction risk management.

Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding ''method uncertainty'' is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.58C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 28C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.

A key challenge in developing flagship climate model configurations is the process of setting uncertain input parameters at values that lead to credible climate simulations. Setting these parameters traditionally relies heavily on insights from those involved in parameterisation of the underlying climate processes. Given the many degrees of freedom and computational expense involved in evaluating such a selection, this can be imperfect leaving open questions about whether any subsequent simulated biases result from mis-set parameters or wider structural model errors (such as missing or partially parameterised processes). Here, we present a complementary approach to identifying plausible climate model parameters, with a method of bias correcting subcomponents of a climate model using a Gaussian process emulator that allows credible values of model input parameters to be found even in the presence of a significant model bias. A previous study (McNeall et al. 2016) found that a climate model had to be run using land surface input parameter values from very different, almost non-overlapping, parts of parameter space to satisfactorily simulate the Amazon and other forests respectively. As the forest fraction of modelled non-Amazon forests was broadly correct at the default parameter settings and the Amazon too low, that study suggested that the problem most likely lay in the model's treatment of non-plant processes in the Amazon region. This might be due to modelling errors such as missing deep rooting in the Amazon in the land surface component of the climate model, to a warm-dry bias in the Amazon climate of the model or a combination of both. In this study, we bias correct the climate of the Amazon in the climate model from McNeall et al. (2016) using an "augmented" Gaussian process emulator, where temperature and precipitation, variables usually regarded as model outputs, are treated as model inputs alongside land surface input parameters. A sensitivity analysis finds that the forest fraction is nearly as sensitive to climate variables as it is to changes in its land surface parameter values. Bias correcting the climate in the Amazon region using the emulator corrects the forest fraction to tolerable levels in the Amazon at many candidates for land surface input parameter values, including the default ones, and increases the valid input space shared with the other forests. We need not invoke a structural model error in the land surface model, beyond having too dry and hot a climate in the Amazon region. The augmented emulator allows bias correction of an ensemble of climate model runs and reduces the risk of choosing poor parameter values because of an error in a subcomponent of the model. We discuss the potential of the augmented emulator to act as a translational layer between model subcomponents, simplifying the process of model tuning when there are compensating errors and helping model developers discover and prioritise model errors to target.

El Niño years are characterized by a high sea surface temperature anomaly in the Equatorial Pacific Ocean, which leads to unusually warm and dry conditions over many fire-prone regions globally. This can lead to an increase in burned area and emissions from fire activity, and socio-economic, and environmental losses. Previous studies using satellite observations to assess the impacts of the recent 2015/16 El Niño found an increase in burned area in some regions compared to La Niña years. Here, we use the dynamic land surface model JULES to assess how conditions differed as a result of the El Niño by comparing simulations driven by observations from the year 2015/16 with mean climatological drivers of temperature, precipitation, humidity, wind, air pressure, and short and long-wave radiation. We use JULES with the interactive fire module INFERNO to assess the effects on precipitation, temperature, burned area, and the associated impacts on the carbon sink globally and for three regions: South America, Africa, and Asia. We find that the model projects a variable response in precipitation, with some areas including northern South America, southern Africa and East Asia getting drier, and most areas globally seeing an increase in temperature. As a result, higher burned area is simulated with El Niño conditions in most regions, although there are areas of both increased and decreased burned area over Africa. South America shows the largest fire response with El Niño, with a 13% increase in burned area and emitted carbon, corresponding with the largest decrease in carbon uptake. Within South America, peak fire occurs from August to October across central-southern Brazil, and temperature is shown to be the main driver of the El Niño-induced increase in burned area during this period. Combined, our results indicate that although 2015/16 was not a peak year for global total burned area or fire emissions, the El Niño led to an overall increase of 4% in burned area and 5% in emissions compared to a “No El Niño” scenario for 2015/16, and contributed to a 4% reduction in the terrestrial carbon sink.

The high-mountain cryosphere forms water towers that are important for ecosystem services provision, supplying large populations living in mountains and the surrounding lowlands and producing potable water resources, and water for agriculture, industry and hydropower generation. However, continued glacier recession and mass loss is projected throughout the twenty-first century, and this raises major concerns regarding the future sustainability of cryospheric water resources. While glacier meltwater represents an essential drought-resilient freshwater resource in vulnerable drought-prone regions, little research has focused on the contribution made by runoff from rock glaciers. These are located widely throughout the high-mountain cryosphere and estimates of rock glacier water volume equivalent (WVEQ) vs glaciers suggests that the former may constitute increasingly important long-term water stores. Owing to the insulating effects of thick supraglacial debris cover, rock glaciers are climatically more resilient than glaciers; therefore, their relative importance versus glaciers may increase under future climate warming. Yet, while the hydrological role of debris-free glaciers and debris-covered glaciers has been the subject of much research, that of rock glaciers has received comparatively little attention. Given the need for strong climate adaptation in many of the world’s mountain regions, it is clear that a more comprehensive understanding of all components of the hydrological cycle in the high-mountain cryosphere is required.

In this thesis, I develop the scientific understanding of rock glacier significance in deglacierizing mountains across a range of spatial scales (local, national, regional and global), with a specific focus on High Mountain Asia (HMA). The review chapter critically assesses the state of current scientific knowledge regarding the hydrological role of rock glaciers in high mountain systems and serves to form the context for the empirical chapters. The thesis has three key themes to which the empirical chapters are aligned: (1) the distribution and hydrological significance of rock glaciers at global scales, (2) the distribution and hydrological significance of rock glaciers at regional and national spatial scales (Himalaya and Nepalese Himalaya), and (3) advancing rock glacier evolutionary theory.

(1) the thesis created a meta-analysis of existing systematic rock glacier inventories and compiled the first near-global rock glacier database (RGDB). The RGDB presented here includes >73,000 rock glaciers (intact = ~39,500, relict = ~33,500), which contain a WVEQ of 83.7 ± 16.7 Gt [~69–102 trillion litres]. Furthermore, the estimated ratio of rock glacier: glacier WVEQ is 1:456 globally.

(2) the results of the meta-analysis described in (1) show that only ~9% of studies included in the RGDB cover the Hindu Kush Himalaya (HKH); therefore, I produced the first systematic rock glacier inventory for the (i) Nepalese Himalaya (national-scale), and (ii) Himalaya (regional-scale). In the former (i) I inventoried >6,000 rock glaciers, and these are estimated to contain a WVEQ of 20.90 ± 4.18 km³ (19.16 ± 3.83 Gt). For the Nepalese Himalaya estimated rock glacier: glacier WVEQ ratio is 1:9. In the latter (ii) ~25,000 rock glaciers have been inventoried. The total WVEQ is 51.80 ± 10.36 km³ (47.48 ± 9.50 Gt) with an estimated rock glacier: glacier WVEQ ratio of 1:24. The results of Theme 1 and 2 indicate that rock glaciers form considerable long-term water stores, which may become increasingly important as climatically-driven glacier recession and mass loss continues throughout the twenty-first century and beyond.

(3) in order to understand debris-free glacier transition to rock glaciers I use in situ sedimentological data and kite aerial photography (KAP) data and develop a conceptual hypothesis to explain the key drivers of this process. The thesis suggests that sediment connectivity (i.e. the strength of the link between sediment sources and downslope landforms) is one such driver of these transition processes. As a consequence, I hypothesise that the presence of well-developed lateral moraines along glacier margins serves to reduce this connectivity, and thus reduce the likelihood of glacier-to-rock glacier transition occurring. The corelationships between rock glaciers and glacial, periglacial and paraglacial processes are also evaluated in the context of rock glacier origin and the changing influence these processes have upon rock glacier evolution through their lifecycle.

Collectively, this research has shaped the understanding of the current and potential future role of rock glaciers in mountain hydrology and is the first to comprehend the distribution and hydrological significance of rock glaciers globally and in the Himalaya.

The Gaia Hypothesis postulates that life and the oceans, crust, and atmosphere of the Earth form a self-regulating planetary-scale system with stabilising properties. Gaia helps to explain the long history of uninterrupted habitability on Earth. Previous Gaian models have uncovered mechanisms for self-regulation in life-environment coupled systems, such as the Earth, and the work in this thesis adds to our understanding on how and when self-regulation can emerge on a planet hosting life, and what conditions help maintain such regulation once established. To place the models presented in this thesis into their proper context this thesis contains background on Earth's history, the history of the Gaia hypothesis and some key Gaian models and known Gaian regulation mechanisms, and a discussion on habitability of exoplanets and our search for alien life.

In this thesis I explore a new variant of a pre-existing Gaian model (the flask model) which demonstrates a new regulation mechanism which I call 'single-rein control'. I then adapt this model to explore the hypothesis of 'selection by survival'. This hypothesis suggests that the longer a life harbouring planet survives, the longer it has to acquire further persistence mechanisms. Therefore over time the only planets hosting life still existing will be those that have acquired several self-regulation mechanisms like those present on Earth. The results of this model demonstrate that selection by survival can promote long term persistence of biospheres compared to a null model.

In the second part of this thesis I consider how the Gaia hypothesis can inform our search for inhabited exoplanets and I introduce the ExoGaia model, a new model of atmospheric regulation where microbes must 'catch' a window of habitability on their host planet, and quickly form self-regulating feedback loops to prevent the planetary temperature from rising to inhospitable levels.

The ExoGaia model demonstrates global regulation and the underlying geochemistry on the planet turns out to be key in determining how robust this regulation is. ExoGaia also demonstrates 'Gaian bottlenecks' where for the same planet life either quickly establishes self-regulating feedback loops and enjoys long term habitability, or fails and becomes extinct, with the host plane quickly reverting to an inhospitable state. This model agrees with the hypothesis that inhabitance and habitability are two sides to the same coin -- that a planet is highly unlike to be in a habitable state, without being inhabited.

This thesis argues a case for 'Probable Homeostatic Gaia' -- that not only is the Earth-system homeostatic but that homeostatic regulation is an expected result of a life-environment coupled system. If true, this would would increase our chances of finding other Gaian worlds.

The Paris agreement aims to hold global warming to well below 2 °C and to pursue efforts to limit it to 1.5 °C relative to the pre-industrial period. Recent estimates based on population growth and intended carbon emissions from participant countries suggest global warming may exceed this ambitious target. Here we present glacier volume projections for the end of this century, under a range of high-end climate change scenarios, defined as exceeding +2° C global average warming relative to the pre-industrial period. Glacier volume is modelled by developing an elevation-dependent mass balance model for the Joint UK Land Environment Simulator (JULES). To do this, we modify JULES to include glaciated and unglaciated surfaces that can exist at multiple heights within a single grid box. Present-day mass balance is calibrated by tuning albedo, wind speed, precipitation, and temperature lapse rates to obtain the best agreement with observed mass balance profiles. JULES is forced with an ensemble of six Coupled Model Intercomparison Project Phase 5 (CMIP5) models, which were downscaled using the high-resolution HadGEM3-A atmosphere-only global climate model. The CMIP5 models use the RCP8.5 climate change scenario and were selected on the criteria of passing 2 °C global average warming during this century. The ensemble mean volume loss at the end of the century plus or minus 1 standard deviation is-64±5% for all glaciers excluding those on the peripheral of the Antarctic ice sheet. The uncertainty in the multi-model mean is rather small and caused by the sensitivity of HadGEM3-A to the boundary conditions supplied by the CMIP5 models. The regions which lose more than 75% of their initial volume by the end of the century are Alaska, western Canada and the US, Iceland, Scandinavia, the Russian Arctic, central Europe, Caucasus, high-mountain Asia, low latitudes, southern Andes, and New Zealand. The ensemble mean ice loss expressed in sea level equivalent contribution is 215.2±21.3 mm. The largest contributors to sea level rise are Alaska (44.6±1.1 mm), Arctic Canada north and south (34.9±3.0 mm), the Russian Arctic (33.3±4.8 mm), Greenland (20.1±4.4), high-mountain Asia (combined central Asia, South Asia east and west), (18.0±0.8 mm), southern Andes (14.4±0.1 mm), and Svalbard (17.0±4.6 mm). Including parametric uncertainty in the calibrated mass balance parameters gives an upper bound global volume loss of 281.1mm of sea level equivalent by the end of the century. Such large ice losses will have inevitable consequences for sea level rise and for water supply in glacier-fed river systems.

Global sustainability is intertwined with freshwater security. Emerging changes in global freshwater availability have been recently detected as a combined result of human interventions, natural variability and climate change. Expected future socio-economic and climatic changes will further impact freshwater resources. The quantification of the impacts is challenging due to the complexity of interdependencies between physical and socio-economic systems. This study demonstrates a vulnerability based assessment of global freshwater availability through a conceptual framework, considering transient hydro-climatic impacts of crossing specific warming levels (1.5 °C, 2 °C and 4 °C) and related socio-economic developments under high-end climate change (RCP8.5). We use high resolution climate scenarios and a global land surface model to develop indicators of exposure for 25,000 watersheds. We also exploit spatially explicit datasets to describe a range of adaptation options through sensitivity and adaptive capacity indicators according to the Shared Socioeconomic Pathways (SSPs). The combined dynamics of climate and socio-economic changes suggest that although there is important potential for adaptation to reduce freshwater vulnerability, climate change risks cannot be totally and uniformly eliminated. In many regions, socio-economic developments will have greater impact on water availability compared to climate induced changes. The number of people under increased freshwater vulnerability varies substantially depending on the level of global warming and the degree of socio-economic developments, from almost 1 billion people at 4 °C and SSP5 to almost 3 billion people at 4 °C and SSP3. Generally, it is concluded that larger adaptation efforts are required to address the risks associated with higher levels of warming of 4 °C compared to the lower levels of 1.5 °C or 2 °C. The watershed scale and country level aggregated results of this study can provide a valuable resource for decision makers to plan for climate change adaptation and mitigation actions.

Fire is an important component of the Earth system, affecting the land surface, releasing gases to the atmosphere, and altering the water cycle. Yet many Earth System Models lack full representation of this process, giving rise to uncertainty about its contribution to the development and stability of ecosystems now and in the future. In this PhD I investigate the impact of fire on the land surface today, and how this might change with drought events and with climate change in the future by developing the land surface model JULES to represent fire-vegetation interactions for the first time. I introduce a new fire disturbance term based on burnt area from the INFERNO fire model, and analyse the results of the coupling, together with changes in land-use, against observations of present day vegetation cover. I find that the simulation of vegetation cover is improved when disturbance is included, and that fire is important in the development of savanna regions. I apply the new modelling capability to assess the impact of the 2015/16 El Niño event on fire, where projections show that burned area and fire emissions were higher due to the El Niño. The largest impact was across South America, where carbon uptake was reduced due to increases in fire, inducing a shift from a net sink of carbon to a net source. Fire danger may be further exacerbated in years of higher temperatures and drought in the future as a result of climate change. I apply the capability to model different aspects of the fire regime with future scenarios of climate and land-use change across a range of emission scenarios. Using Representative Concentration Pathway scenarios, I show that burned area is projected to increase in the future, with hotter, drier conditions increasing with higher emission scenarios and greater changes in land-use, especially across South America but not homogeneously. Using a theoretical scenario of Solar Radiation Management to limit temperature rise to 1.5°C above pre-industrial, I show that meteorological fire danger is generally reduced compared to 2.0°C, although there are regional variations and some regions show an increase including USA and Asia. This work furthers our current modelling capability around fire vegetation interactions, and enhances our understanding of the response of ecosystems to changes in fire, climate and land-use.

Changing climate conditions impact ecosystem dynamics and have local to global impacts on water and carbon cycles. Many processes in dynamic vegetation models (DVMs) are parameterized, and the unknown/unknowable parameter values introduce uncertainty that has rarely been quantified in projections of forced changes. In this study, we identify processes and parameters that introduce the largest uncertainties in the vegetation state simulated by the DVM Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) coupled to a regional climate model. We adjust parameters simultaneously in an ensemble of equilibrium vegetation simulations and use statistical emulation to explore sensitivities to, and interactions among, parameters. We find that vegetation distribution is most sensitive to parameters related to carbon allocation and competition. Using a suite of statistical emulators, we identify regions of parameter space that reduce the error in modeled forest cover by 31±9%. We then generate large initial atmospheric condition ensembles with 10 improved DVM parameterizations under preindustrial, contemporary, and future climate conditions to assess uncertainty in the forced response due to parameterization. We find that while most parameterizations agree on the direction of future vegetation transitions in the western United States, the magnitude varies considerably: for example, in the northwest coast the expansion of broadleaf trees and corresponding decline of needleleaf trees ranges from 4 to 28% across 10 DVM parameterizations under projected future climate conditions. We demonstrate that model parameterization contributes to uncertainty in vegetation transition and carbon cycle feedback under nonstationary climate conditions, which has important implications for carbon stocks, ecosystem services, and climate feedback.

Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global-regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74% of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

Disturbance of vegetation is a critical component of land cover, but is generally poorly constrained in land surface and carbon cycle models. In particular, land-use change and fire can be treated as large-scale disturbances without full representation of their underlying complexities and interactions. Here we describe developments to the land surface model JULES (Joint UK Land Environment Simulator) to represent land-use change and fire as distinct processes which interact with simulated vegetation dynamics. We couple the fire model INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) to dynamic vegetation within JULES and use the HYDE (History Database of the Global Environment) land cover dataset to analyse the impact of land-use change on the simulation of present day vegetation. We evaluate the inclusion of land use and fire disturbance against standard benchmarks. Using the Manhattan metric, results show improved simulation of vegetation cover across all observed datasets. Overall, disturbance improves the simulation of vegetation cover by 35 % compared to vegetation continuous field (VCF) observations from MODIS and 13 % compared to the Climate Change Initiative (CCI) from the ESA. Biases in grass extent are reduced from ĝ'66 % to 13 %. Total woody cover improves by 55 % compared to VCF and 20 % compared to CCI from a reduction in forest extent in the tropics, although simulated tree cover is now too sparse in some areas. Explicitly modelling fire and land use generally decreases tree and shrub cover and increases grasses. The results show that the disturbances provide important contributions to the realistic modelling of vegetation on a global scale, although in some areas fire and land use together result in too much disturbance. This work provides a substantial contribution towards representing the full complexity and interactions between land-use change and fire that could be used in Earth system models.

In early 2016, we predicted that the annual rise in carbon dioxide concentration at Mauna Loa would be the largest on record. Our forecast used a statistical relationship between observed and forecast sea surface temperatures in the Niño 3.4 region and the annual CO2 rise. Here, we provide a formal verification of that forecast. The observed rise of 3.4 ppm relative to 2015 was within the forecast range of 3.15 ± 0.53 ppm, so the prediction was successful. A global terrestrial biosphere model supports the expectation that the El Niño weakened the tropical land carbon sink. We estimate that the El Niño contributed approximately 25% to the record rise in CO2, with 75% due to anthropogenic emissions. The 2015/2016 CO2 rise was greater than that following the previous large El Niño in 1997/1998, because anthropogenic emissions had increased. We had also correctly predicted that 2016 would be the first year with monthly mean CO2 above 400 ppm all year round. We now estimate that atmospheric CO2 at Mauna Loa would have remained above 400 ppm all year round in 2016 even if the El Niño had not occurred, contrary to our previous expectations based on a simple extrapolation of previous trends.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

A wide range of climate vulnerability and risk assessments have been implemented using different approaches at different scales, some with a broad multi-sectoral scope and others focused on single risks or sectors. This paper describes the novel approach to vulnerability and risk assessment which was designed and put into practice in the United Kingdom's Second Climate Change Risk Assessment (CCRA2) so as to build upon its earlier assessment (CCRA1). First, we summarize and critique the CCRA1 approach, and second describe the steps taken in the CCRA2 approach in detail, providing examples of how each was applied in practice. Novel elements of the approach include assessment of both present day and future vulnerability, a focus on the urgency of adaptation action, and a structure focused around systems of receptors rather than conventional sectors. Both stakeholders and reviewers generally regarded the approach as successful in providing advice on current risks and future opportunities to the UK from climate change, and the fulfilment of statutory duty. The need for a well-supported and open suite of impact indicators going forward is highlighted.This article is part of the theme issue 'Advances in risk assessment for climate change adaptation policy'.

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

In the version of this Letter originally published, the affiliation for Yukiko Hirabayashi was mistakenly given as ‘Institute of Industrial Science, the University of Tokyo, Bunkyō, Japan’. It should have read ‘Department of Civil Engineering, Shibaura Institute of Technology, Tokyo, Japan’. This has now been corrected.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere-the "global carbon budget"-is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1δ. For the last decade available (2007-2016), EFF was 9.4±0.5 GtC yr-1, ELUC 1.3±0.7 GtC yr-1, GATM 4.7±0.1 GtC yr-1, SOCEAN 2.4±0.5 GtC yr-1, and SLAND 3.0±0.8 GtC yr-1, with a budget imbalance BIM of 0.6 GtC yr-1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9±0.5 GtC yr-1. Also for 2016, ELUC was 1.3±0.7 GtC yr-1, GATM was 6.1±0.2 GtC yr-1, SOCEAN was 2.6±0.5 GtC yr-1, and SLAND was 2.7±1.0 GtC yr-1, with a small BIM of-0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007-2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Ninõ conditions. The global atmospheric CO2 concentration reached 402.8±0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6-9 months indicate a renewed growth in EFF of C2.0% (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al. 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

Higher evaporative demands and more frequent and persistent dry spells associated with rising temperatures suggest that drought conditions could worsen in many regions of the world. In this study, we assess how drought conditions may develop across the globe for 1.5, 2, and 3°C warming compared to preindustrial temperatures. Results show that two thirds of global population will experience a progressive increase in drought conditions with warming. For drying areas, drought durations are projected to rise at rapidly increasing rates with warming, averaged globally from 2.0 month/°C below 1.5°C to 4.2 month/°C when approaching 3°C. Drought magnitudes could double for 30% of global landmass under stringent mitigation. If contemporary warming rates continue, water supply-demand deficits could become fivefold in size for most of Africa, Australia, southern Europe, southern and central states of the United States, Central America, the Caribbean, north-west China, and parts of Southern America. In approximately 20% of the global land surface, drought magnitude will halve with warming of 1.5°C and higher levels, mainly most land areas north of latitude 55°N, but also parts of South America and Eastern and South-eastern Asia. A progressive and significant increase in frequency of droughts is projected with warming in the Mediterranean basin, most of Africa, West and Southern Asia, Central America, and Oceania, where droughts are projected to happen 5 to 10 times more frequent even under ambitious mitigation targets and current 100-year events could occur every two to five years under 3°C of warming.

Abstract. The Paris agreement aims to hold global warming to well below 2 °C and to pursue efforts to limit it to 1.5 °C relative to the pre-industrial period. Recent estimates based on population growth and intended carbon emissions from participant countries, suggest global warming may exceed this ambitious target. Here we present glacier volume projections for the end of this century, under a range of high-end climate change scenarios, defined as exceeding +2 °C global average warming relative to the preindustrial period. Glacier volume is modelled by developing an elevation-dependent mass balance model for the Joint UK Land Environmental Simulator (JULES). To do this, we modify JULES to include glaciated and un-glaciated surfaces that can exist at multiple heights within a single grid-box. Present day mass balance is calibrated by tuning albedo, wind speed, precipitation and temperature lapse rates to obtain the best agreement with observed mass balance profiles. JULES is forced with an ensemble of six Coupled Model Intercomparison Project Phase 5 (CMIP5) models which were downscaled using the high resolution HadGEM3-A atmosphere only global climate model. The ensemble mean volume loss at the end of the century plus/minus one standard deviation is, minus;64 ± 5 % for all glaciers excluding those on the peripheral of the Antarctic ice sheet. The uncertainty in the multi-model mean is rather small and caused by the sensitivity of HadGEM3-A to the boundary conditions supplied by the CMIP5 models. The regions which lose more than 75% of their initial volume by the end of the century are; Alaska, Western Canada and US, Iceland, Scandinavia, Russian Arctic, Central Europe, Caucasus, High Mountain Asia, Low Latitudes, Southern Andes and New Zealand. The ensemble mean ice loss expressed in sea-level equivalent contribution is 215.2 ± 21.3 mm. The largest contributors to sea level rise are Alaska (44.6 ± 1.1 mm), Arctic Canada North and South (34.9 ± 3.0 mm), Russian Arctic (33.3 ± 4.8 mm), Greenland (20.1 ± 4.4), High Mountain Asia (combined Central Asia, South Asia East and West), (18.0 ± 0.8 mm), Southern Andes (14.4 ± 0.1 mm) and Svalbard (17.0 ± 4.6 mm). Including parametric uncertainty in the calibrated mass balance parameters, gives an upper bound global volume loss of 247.3 mm, sea-level equivalent by the end of the century. Such large ice losses will have inevitable consequences for sea-level rise and for water supply in glacier-fed river systems.
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The atmospheric concentration of CO2 at the time of passing 1.5 °C or 2 °C is unknown due to uncertainties in climate sensitivity and the concentrations of other GHGs. Impacts studies must account for a wide range of concentrations to avoid either over-or underestimating changes in crop yields and land and marine biodiversity.

River floods are among some of the costliest natural disasters1, but their socio-economic impacts under contrasting warming levels remain little explored2. Here, using a multi-model framework, we estimate human losses, direct economic damage and subsequent indirect impacts (welfare losses) under a range of temperature (1.5 °C, 2 °C and 3 °C warming)3 and socio-economic scenarios, assuming current vulnerability levels and in the absence of future adaptation. With temperature increases of 1.5 °C, depending on the socio-economic scenario, it is found that human losses from flooding could rise by 70–83%, direct flood damage by 160–240%, with a relative welfare reduction between 0.23 and 0.29%. In a 2 °C world, by contrast, the death toll is 50% higher, direct economic damage doubles and welfare losses grow to 0.4%. Impacts are notably higher under 3 C warming, but at the same time, variability between ensemble members also increases, leading to greater uncertainty regarding flood impacts at higher warming levels. Flood impacts are further shown to have an uneven regional distribution, with the greatest losses observed in the Asian continent at all analysed warming levels. It is clear that increased adaptation and mitigation efforts—perhaps through infrastructural investment4—are needed to offset increasing risk of river floods in the future.

The simulation of hydrological impacts in a changing climate remains one of the main challenges of the earth system sciences. Impact assessments can be, in many cases, laborious processes leading to inevitable methodological compromises that drastically affect the robustness of the conclusions. In this study we examine the implications of different CMIP5-based regional and global climate model ensembles for projections of the hydrological impacts of climate change. We compare results from three different assessments of hydrological impacts under high-end climate change (RCP8.5) across Europe, and we focus on how methodological differences affect the projections. We assess, as systematically as possible, the differences in runoff projections as simulated by a land surface model driven by three different sets of climate projections over the European continent at global warming of 1.5 °C, 2 °C and 4 °C relative to pre-industrial levels, according to the RCP8.5 concentration scenario. We find that these methodological differences lead to considerably different outputs for a number of indicators used to express different aspects of runoff. We further use a number of new global climate model experiments, with an emphasis on high resolution, to test the assumption that many of the uncertainties in regional climate and hydrological changes are driven predominantly by the prescribed sea surface temperatures (SSTs) and sea-ice concentrations (SICs) and we find that results are more sensitive to the choice of the atmosphere model compared to the driving SSTs. Finally, we combine all sources of information to identify robust patterns of hydrological changes across the European continent.

Abstract. Despite recent research identifying a clear anthropogenic impact on glacier recession, the effect of recent climate change on glacier-related hazards is at present unclear. Here we present the first global spatio-temporal assessment of glacial lake outburst floods (GLOFs) focusing explicitly on lake drainage following moraine dam failure. These floods occur as mountain glaciers recede and downwaste and many have an enormous impact on downstream communities and infrastructure. Our assessment of GLOFs associated with the collapse of moraine-dammed lakes provides insights into the historical trends of GLOFs and their distributions under current and future global climate change. We observe a clear global increase in GLOF frequency and their regularity around 1930, which likely represents a lagged response to post-Little Ice Age warming. Notably, we also show that GLOF frequency and their regularity – rather unexpectedly – has declined in recent decades even during a time of rapid glacier recession. Although previous studies have suggested that GLOFs will increase in response to climate warming and glacier recession, our global results demonstrate that this has not yet clearly happened. From assessment of the timing of climate forcing, lag times in glacier recession, lake formation and moraine dam failure, we predict increased GLOF frequencies during the next decades and into the 22nd century.
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The effective radiative forcing (ERF) from the biogeophysical effects of historical land use change is quantified using the atmospheric component of the Met Office Hadley Centre Earth System model HadGEM2-ES. The global ERF at 2005 relative to 1860 (1700) is −0.4 (−0.5) Wm−2, making it the fourth most important anthropogenic driver of climate change over the historical period (1860–2005) in this model and larger than most other published values. The land use ERF is found to be dominated by increases in the land surface albedo, particularly in North America and Eurasia, and occurs most strongly in the northern hemisphere winter and spring when the effect of unmasking underlying snow, as well as increasing the amount of snow, is at its largest. Increased bare soil fraction enhances the seasonal cycle of atmospheric dust and further enhances the ERF. Clouds are shown to substantially mask the radiative effect of changes in the underlying surface albedo. Coupled atmosphere–ocean simulations forced only with time-varying historical land use change shows substantial global cooling (dT = −0.35 K by 2005) and the climate resistance (ERF/dT = 1.2 Wm−2 K−1) is consistent with the response of the model to increases in CO2 alone. The regional variation in land surface temperature change, in both fixed-SST and coupled atmosphere–ocean simulations, is found to be well correlated with the spatial pattern of the forced change in surface albedo. The forcing-response concept is found to work well for historical land use forcing—at least in our model and when the forcing is quantified by ERF. Our results suggest that land-use changes over the past century may represent a more important driver of historical climate change then previously recognised and an underappreciated source of uncertainty in global forcings and temperature trends over the historical period.

Impacts of climate extremes in Brazil led to the development of a web platform for understanding long-term sustainability of ecosystems and human health in Amazonia. It was difficult to synthesize all available information in a comprehensive structure that enables different sectors of the society to understand the consequences of extreme events and support timely decision making. In recognition of this problem of data compilation, management, and visualization, a consortium of cross-disciplinary Brazilian and UK scientists, encompassing environmental, human health, and modeling backgrounds, was selected under the umbrella of the International Opportunities Fund, and jointly funded by the São Paulo Science Foundation (FAPESP) in Brazil and the Natural Environment Research Council (NERC) in the United Kingdom to enhance the knowledge in environmental sciences directly applicable to policy decisions. A key result of this cooperation was the ongoing development of PULSE-Brazil, a Platform for Understanding Long-term Sustainability of Ecosystems and human health, specifically applied to Brazil.

Abstract. Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.
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© 2015 John Wiley & Sons Ltd. Aim: the spatial pattern of tropical fire-induced tree mortality is partly determined by climate, but feedbacks of tree cover on fire are also important. We re-examine some recent observations proposed as evidence for very strong tree-cover feedbacks on fire, sufficient to allow savanna and forest to be alternative stable states over large areas of the tropics. Two pieces of previously reported observational evidence are examined: (1) the trimodal statistical distribution of tropical tree fraction, and (2) the fact that different tree fractional cover is found at different locations in the tropics with similar rainfall. Location: Global tropics. Methods: for point (1) above we analyse the statistical distribution of tree fraction predicted by the logistic equation of tree growth and self-competition, with spatially varying mortality rates. For (2), the relationship between mean annual rainfall and mean net primary productivity (NPP) in a climate model is examined. Results: (1) a trimodal distribution of tree cover does not necessarily require tree-cover feedback on fire. It can arise from a combination of two factors: nonlinearities in vegetation dynamics and climate-driven spatial variation in mortality (the intermediate fire-productivity hypothesis). (2) Different locations in the tropics can have identical rainfall but significantly different NPP, even with no feedback of tree cover on fire. Main conclusions: Our results show that strong tree-cover feedback on fire is not necessary to explain observations (1) and (2). However, it is still possible that strong fire-vegetation feedback is the primary explanation - our results do not rule this out. We simply demonstrate the possibility of an alternative hypothesis (of strong climate control). In reality, it is likely that both tree-cover feedback and climate contribute. It is challenging to separate these two effects cleanly. More work is needed to quantify their separate effects. We show that plots of mortality versus productivity are useful tools for understanding spatial variations in tree cover.

A new generation of an Earth system model now includes a number of land-surface processes directly relevant to analyzing potential impacts of climate change. This model, HadGEM2-ES, allows us to assess the impacts of climate change, multiple interactions, and feedbacks as the model is run. This paper discusses the results of century-scale HadGEM2-ES simulations from an impacts perspective - specifically, terrestrial ecosystems and water resources - for four different scenarios following the representative concentration pathways (RCPs), used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2013, 2014). Over the 21st century, simulated changes in global and continental-scale terrestrial ecosystems due to climate change appear to be very similar in all 4 RCPs, even though the level of global warming by the end of the 21st century ranges from 2°C in the lowest scenario to 5.5° in the highest. A warming climate generally favours broadleaf trees over needleleaf, needleleaf trees over shrubs, and shrubs over herbaceous vegetation, resulting in a poleward shift of temperate and boreal forests and woody tundra in all scenarios. Although climate related changes are slightly larger in scenarios of greater warming, the largest differences between scenarios arise at regional scales as a consequence of different patterns of anthropogenic land cover change. In the model, the scenario with the lowest global warming results in the most extensive decline in tropical forest cover due to a large expansion of agriculture. Under all four RCPs, fire potential could increase across extensive land areas, particularly tropical and sub-tropical latitudes. River outflows are simulated to increase with higher levels of CO2 and global warming in all projections, with outflow increasing with mean temperature at the end of the 21st century at the global scale and in North America, Asia, and Africa. In South America, Europe, and Australia, the relationship with climate warming and CO2 rise is less clear, probably as a result of land cover change exerting a dominant effect in those regions.

Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soybean, maize and rice. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soybean at the global and country levels, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index, gross primary production and canopy height better than in the standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an Earth system and crop yield model perspective is encouraging. However, more effort is needed to develop the parametrisation of the model for specific applications. Key future model developments identified include the introduction of processes such as irrigation and nitrogen limitation which will enable better representation of the spatial variability in yield.

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.
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. Future climate change and increasing atmospheric CO
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. are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510–758 ppm of CO
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. ), vegetation carbon increases by 52–477 Pg C (224 Pg C mean), mainly due to CO
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. fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended.
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Net primary productivity (NPP) is often modelled explicitly in general circulation models (GCMs) utilising process models that may include plant photosynthesis, respiration, allocation of photosynthates, phenology, mortality and competition between plant functional types. It is an important measure for understanding the role of terrestrial vegetation in the global carbon cycle, and useful for gaining insights into the large-scale, integrated effects of climate and atmospheric changes on potential plant productivity and associated impacts, i.e. food security and carbon cycle feedbacks. However, there are simplifications and uncertainties in GCM projections of future climate change, as well as further uncertainties involved in modelling the associated terrestrial vegetation responses. In particular, it is important to highlight that many GCM simulations, including the ones used in this study, do not model nutrient limitation, even though primary plant nutrients, e.g. nitrogen and phosphorus, are key limiting factors on plant productivity.Here, we examine sensitivities and uncertainties in large(global)-scale modelled NPP to climate and atmospheric carbon dioxide concentration [CO2], utilising a relatively large perturbed physics ensemble (PPE) of simulations generated from the HadSM3 GCM under equilibrium doubling of pre-industrial atmospheric [CO2]. We also exploit the ensemble design to highlight the relative importance of two, often opposing, forcings on NPP: (i) plant physiological responses to CO2, termed 'Phys'; and (ii) plant responses to physical drivers of climate, termed 'Rad'. It is important to note that this is a sensitivity study that provides useful guidance on the relative importance of the Rad and Phys drivers and their uncertainties. The results cannot be considered quantitatively realistic, particularly because the equilibrium experimental design and lack of nutrient limitation in the model are important limitations that prevent such interpretation.We find that doubled [CO2] and associated climate changes ultimately increase potential global average NPP by 57%, from 0.293kgcm-2yr-1 (~36PgCyr-1) to 0.460kgcm-2yr-1 (~57PgCyr-1). Spatially, the largest decreases (~-0.45kgcm-2yr-1) occur across the north-east of South America in association with the largest decreases in precipitation. The largest increases (up to ~0.75kgcm-2yr-1) occur across tropical Africa and Indonesia, where NPP is already high, and both temperature and precipitation increase under doubled [CO2]. In most regions where NPP shows an increase the changes are significantly larger than the ensemble standard deviation, indicating that increases in global NPP under doubled [CO2] are reasonably robust. However, in some regions, particularly north-eastern South America and Central America, where NPP decreases are projected, the standard deviation across the ensemble is larger than the average NPP change, indicating that even the sign of the NPP sensitivity to doubled [CO2] and climate is uncertain. These uncertainties are shown to be highly dependent on the relative sensitivities of NPP to the Phys and Rad forcings. © 2011.

How tropical forest carbon stocks might alter in response to changes in climate and atmospheric composition is uncertain. However, assessing potential future carbon loss from tropical forests is important for evaluating the efficacy of programmes for reducing emissions from deforestation and degradation. Uncertainties are associated with different carbon stock responses in models with different representations of vegetation processes on the one hand, and differences in projected changes in temperature and precipitation patterns on the other hand. Here we present a systematic exploration of these sources of uncertainty, along with uncertainty arising from different emissions scenarios for all three main tropical forest regions: the Americas (that is, Amazonia and Central America), Africa and Asia. Using simulations with 22 climate models and the MOSES-TRIFFID land surface scheme, we find that only in one of the simulations are tropical forests projected to lose biomass by the end of the twenty-first century - and then only for the Americas. When comparing with alternative models of plant physiological processes, we find that the largest uncertainties are associated with plant physiological responses, and then with future emissions scenarios. Uncertainties from differences in the climate projections are significantly smaller. Despite the considerable uncertainties, we conclude that there is evidence of forest resilience for all three regions. © 2013 Macmillan Publishers Limited. All rights reserved.

Population growth and climate change are likely to impact upon food and water availability over the coming decades. In this study we use an ensemble of climate simulations to project the implications of both these drivers on regional changes in food and water. This study highlights the dominant effect of population growth on per capita resource allocation over climate induced changes in our model projections. We find a strong signal for crop yield reductions due to climate change by the 2050s in the absence of CO2 fertilisation effects. However, when these additional processes are included this trend is reversed. The impacts of climate on water resources are more uncertain. Overall, we find reductions in the global population living in water stressed conditions due to the combined effects of climate and CO2. Africa is a key region where projected decreases in runoff and crop productivity from climate change alone are potentially reversed when CO2 fertilisation effects are included, but this is highly uncertain. Plant physiological response to increasing atmospheric CO2 is a major driver of the changes in crop productivity and water availability in this study; it is poorly constrained by observations and is thus a critical uncertainty. © 2013 by the authors.

Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climatecarbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climatecarbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change. © 2012 IOP Publishing Ltd.

The aim of our study was to use the coupled climate-carbon cycle model HadCM3C to quantify climate impact of ecosystem changes over recent decades and under future scenarios, due to changes in both atmospheric CO2 and surface albedo. We use two future scenarios-the IPCC SRES A1B scenario, and a climate stabilisation scenario (2C20), allowing us to assess the impact of climate mitigation on Results. We performed a pair of simulations under each scenario-one in which vegetation was fixed at the initial state and one in which vegetation changes dynamically in response to climate change, as determined by the interactive vegetation model within HadCM3C. In our simulations with interactive vegetation, relatively small changes in global vegetation coverage were found, mainly dominated by increases in shrub and needleleaf trees at high latitudes and losses of broadleaf trees and grasses across the Amazon. Globally this led to a loss of terrestrial carbon, mainly from the soil. Global changes in carbon storage were related to the regional losses from the Amazon and gains at high latitude. Regional differences in carbon storage between the two scenarios were largely driven by the balance between warming-enhanced decomposition and altered vegetation growth. Globally, interactive vegetation reduced albedo acting to enhance albedo changes due to climate change. This was mainly related to the darker land surface over high latitudes (due to vegetation expansion, particularly during December-January and March-May); small increases in albedo occurred over the Amazon. As a result, there was a relatively small impact of vegetation change on most global annual mean climate variables, which was generally greater under A1B than 2C20, with markedly stronger local-to-regional and seasonal impacts. Globally, vegetation change amplified future annual temperature increases by 0.24 and 0.15 K (under A1B and 2C20, respectively) and increased global precipitation, with reductions in precipitation over the Amazon and increases over high latitudes. In general, changes were stronger over land-for example, global temperature changes due to interactive vegetation of 0.43 and 0.28 K under A1B and 2C20, respectively. Regionally, the warming influence of future vegetation change in our simulations was driven by the balance between driving factors. For instance, reduced tree cover over the Amazon reduced evaporation (particularly during June-August), outweighing the cooling influence of any small albedo changes. In contrast, at high latitudes the warming impact of reduced albedo (particularly during December-February and March-May) due to increased vegetation cover appears to have offset any cooling due to small evaporation increases. Climate mitigation generally reduced the impact of vegetation change on future global and regional climate in our simulations. Our study therefore suggests that there is a need to consider both biogeochemical and biophysical effects in climate adaptation and mitigation decision making. © 2012 Author(s).

The role of global vegetation on the large-scale tropical circulation is examined in the version 3 Hadley Centre climate model (HadCM3). Alternative representations of global vegetation cover from observations and a dynamic global vegetation model (DGVM) were used as the landcover component for a number of HadCM3 experiments under a nominal present day climate state, and compared to the simulations using the standard land cover map of HadCM3. The alternative vegetation covers result in a large scale cooling of the Northern Hemisphere extra-tropics relative to the HadCM3 standard, resulting in a southward shift in the location of the inter-tropical convergence zone (ITCZ). A significant reduction in Indian monsoon precipitation is also found, which is related to a weakening of the South Asian monsoon circulation, broadly consistent with documented mechanisms relating to temperature and snow perturbations in the Northern Hemisphere extra-tropics in winter and spring, delaying the onset of the monsoon. The role of the Northern Hemisphere extra-tropics on tropical climate is demonstrated, with an additional representation of vegetation cover based on DGVM simulated changes in Northern Hemisphere vegetation from the end of the 21st Century. This experiment shows that through similar processes the simulated extra-tropical vegetation changes in the future contribute to a strengthening of the South Asian monsoon in this model. These findings provide renewed motivation to give careful consideration to the role of global scale vegetation feedbacks when looking at climate change, and its impact on the tropical circulation and South Asian monsoon in the latest generation of Earth System models. © Author(s) 2012.

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e. g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005-2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500-2100 at 0.5° × 0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500-2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005-2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. © 2011 the Author(s).

Climate models vary widely in their projections of both global mean temperature rise and regional climate changes but are there any systematic differences in regional changes associated with different levels of global climate sensitivity? This paper examines model projections of climate change over the twenty-first century from the Intergovernmental Panel on Climate Change Fourth Assessment Report which used the A2 scenario from the IPCC Special Report on Emissions Scenarios assessing whether different regional responses can be seen in models categorized as 'high-end' (those projecting 4° C or more by the end of the twenty-first century relative to the preindustrial). It also identifies regions where the largest climate changes are projected under high-end warming. The mean spatial patterns of change normalized against the global rate of warming are generally similar in high-end and 'non-high-end' simulations. The exception is the higher latitudes where land areas warm relatively faster in boreal summer in high-end models but sea ice areas show varying differences in boreal winter. Many continental interiors warm approximately twice as fast as the global average with this being particularly accentuated in boreal summer and the winter-time Arctic Ocean temperatures rise more than three times faster than the global average. Large temperature increases and precipitation decreases are projected in some of the regions that currently experience water resource pressures including Mediterranean fringe regions indicating enhanced pressure on water resources in these areas. ©2011 the Royal Society.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) assessed a range of scenarios of future greenhouse-gas emissions without policies to specifically reduce emissions, and concluded that these would lead to an increase in global mean temperatures of between 1.6°C and 6.9°C by the end of the twenty-first century, relative to pre-industrial. While much political attention is focused on the potential for global warming of 2°C relative to pre-industrial, the AR4 projections clearly suggest that much greater levels of warming are possible by the end of the twenty-first century in the absence of mitigation. The centre of the range of AR4-projected global warming was approximately 4°C. The higher end of the projected warming was associated with the higher emissions scenarios and models, which included stronger carbon-cycle feedbacks. The highest emissions scenario considered in the AR4 (scenario A1FI) was not examined with complex general circulation models (GCMs) in the AR4, and similarly the uncertainties in climate-carbon-cycle feedbacks were not included in the main set of GCMs. Consequently, the projections of warming for A1FI and/or with different strengths of carbon-cycle feedbacks are often not included in a wider discussion of the AR4 conclusions. While it is still too early to say whether any particular scenario is being tracked by current emissions, A1FI is considered to be as plausible as other non-mitigation scenarios and cannot be ruled out. (A1FI is a part of the A1 family of scenarios, with 'FI' standing for 'fossil intensive'. This is sometimes erroneously written as A1F1, with number 1 instead of letter I.) This paper presents simulations of climate change with an ensemble of GCMs driven by the A1FI scenario, and also assesses the implications of carbon-cycle feedbacks for the climate-change projections. Using these GCM projections along with simple climate-model projections, including uncertainties in carbon-cycle feedbacks, and also comparing against other model projections from the IPCC, our best estimate is that the A1FI emissions scenario would lead to a warming of 4°C relative to pre-industrial during the 2070s. If carbon-cycle feedbacks are stronger, which appears less likely but still credible, then 4°C warming could be reached by the early 2060s in projections that are consistent with the IPCC's 'likely range'.

On palaeoclimate time scales, enhanced levels of geological and geomorphological activity have been linked to climatic factors, including examples of processes that are expected to be important in current and future anthropogenic climate change. Planetary warming leading to increased rainfall, ice-mass loss and rising sea levels is potentially relevant to geospheric responses in many geologically diverse regions. Anthropogenic climate change, therefore, has the potential to alter the risk of geological and geomorphological hazards through the twenty-first century and beyond. Here, we review climate change projections from both global and regional climate models in the context of geohazards. In assessing the potential for geospheric responses to climate change, it appears prudent to consider regional levels of warming of 2 degrees C above average pre-industrial temperature as being potentially unavoidable as an influence on processes requiring a human adaptation response within this century. At the other end of the scale when considering changes that could be avoided by reduction of emissions, scenarios of unmitigated warming exceeding 4 degrees C in the global average include much greater local warming in some regions. However, considerable further work is required to better understand the uncertainties associated with these projections, uncertainties inherent not only in the climate modelling but also in the linkages between climate change and geospheric responses.

Loss of large areas of Amazonian forest, through either direct human impact or climate change, could exert a number of influences on the regional and global climates. In the Met Office Hadley Centre coupled climate-carbon cycle model, a severe drying of this region initiates forest loss that exerts a number of feedbacks on global and regional climates, which magnify the drying and the forest degradation. This paper provides an overview of the multiple feedback process in the Hadley Centre model and discusses the implications of the results for the case of direct human-induced deforestation. It also examines additional potential effects of forest loss through changes in the emissions of mineral dust and biogenic volatile organic compounds. The implications of ecosystem-climate feedbacks for climate change mitigation and adaptation policies are also discussed.

This study tests the ability of five Dynamic Global Vegetation Models (DGVMs), forced with observed climatology and atmospheric CO2, to model the contemporary global carbon cycle. The DGVMs are also coupled to a fast 'climate analogue model', based on the Hadley Centre General Circulation Model (GCM), and run into the future for four Special Report Emission Scenarios (SRES): A1FI, A2, B1, B2. Results show that all DGVMs are consistent with the contemporary global land carbon budget. Under the more extreme projections of future environmental change, the responses of the DGVMs diverge markedly. In particular, large uncertainties are associated with the response of tropical vegetation to drought and boreal ecosystems to elevated temperatures and changing soil moisture status. The DGVMs show more divergence in their response to regional changes in climate than to increases in atmospheric CO2 content. All models simulate a release of land carbon in response to climate, when physiological effects of elevated atmospheric CO2 on plant production are not considered, implying a positive terrestrial climate-carbon cycle feedback. All DGVMs simulate a reduction in global net primary production (NPP) and a decrease in soil residence time in the tropics and extra-tropics in response to future climate. When both counteracting effects of climate and atmospheric CO2 on ecosystem function are considered, all the DGVMs simulate cumulative net land carbon uptake over the 21st century for the four SRES emission scenarios. However, for the most extreme A1FI emissions scenario, three out of five DGVMs simulate an annual net source of CO2 from the land to the atmosphere in the final decades of the 21st century. For this scenario, cumulative land uptake differs by 494 Pg C among DGVMs over the 21st century. This uncertainty is equivalent to over 50 years of anthropogenic emissions at current levels.

The Amazon rainforest plays a crucial role in the climate system, helping to drive atmospheric circulations in the tropics by absorbing energy and recycling about half of the rainfall that falls on it. This region (Amazonia) is also estimated to contain about one-tenth of the total carbon stored in land ecosystems, and to account for one-tenth of global, net primary productivity. The resilience of the forest to the combined pressures of deforestation and global warming is therefore of great concern, especially as some general circulation models (GCMs) predict a severe drying of Amazonia in the twenty-first century. Here we analyse these climate projections with reference to the 2005 drought in western Amazonia, which was associated with unusually warm North Atlantic sea surface temperatures (SSTs). We show that reduction of dry-season (July-October) rainfall in western Amazonia correlates well with an index of the north-south SST gradient across the equatorial Atlantic (the 'Atlantic N-S gradient'). Our climate model is unusual among current GCMs in that it is able to reproduce this relationship and also the observed twentieth-century multidecadal variability in the Atlantic N-S gradient, provided that the effects of aerosols are included in the model. Simulations for the twenty-first century using the same model show a strong tendency for the SST conditions associated with the 2005 drought to become much more common, owing to continuing reductions in reflective aerosol pollution in the Northern Hemisphere.

The potential loss or large-scale degradation of the tropical rainforests has become one of the iconic images of the impacts of twenty-first century environmental change and may be one of our century's most profound legacies. In the Amazon region, the direct threat of deforestation and degradation is now strongly intertwined with an indirect challenge we are just beginning to understand: the possibility of substantial regional drought driven by global climate change. The Amazon region hosts more than half of the world's remaining tropical forests, and some parts have among the greatest concentrations of biodiversity found anywhere on Earth. Overall, the region is estimated to host about a quarter of all global biodiversity. It acts as one of the major 'flywheels' of global climate, transpiring water and generating clouds, affecting atmospheric circulation across continents and hemispheres, and storing substantial reserves of biomass and soil carbon. Hence, the ongoing degradation of Amazonia is a threat to local climate stability and a contributor to the global atmospheric climate change crisis. Conversely, the stabilization of Amazonian deforestation and degradation would be an opportunity for local adaptation to climate change, as well as a potential global contributor towards mitigation of climate change. However, addressing deforestation in the Amazon raises substantial challenges in policy, governance, sustainability and economic science. This paper introduces a theme issue dedicated to a multidisciplinary analysis of these challenges.

Climate scenarios provide an essential foundation for research on the impacts of climate change on the built environment and for the identification of appropriate adaptation measures. They are, however, subject to uncertainties in the underlying greenhouse gas emissions and concentration scenarios as well as a range of scientific uncertainties associated with climate modelling and the natural variability of climate. These uncertainties provide a major motive for the current move towards probabilistic climate scenarios - a move which is also supported from the decision-making perspective. Examples of probabilistic scenarios constructed for variables and UK locations of interest for built environment research are presented here. The need to consider other uncertainties - potentially important sub-grid scale processes such as the urban heat island effect and the influence of natural variability in non-stationary series of weather extremes - is demonstrated. Consideration is also given to aspects of decision making under uncertainty - focusing on the timing of upgrade of the Thames tidal defences and the extent to which managers integrate climate change information into long-term projects in the built environment. A major recommendation from the case-study examples presented here is the need for improved communication between climate scenario developers, and scenario users and decision makers in the built environment sector.

In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology. Plant stomata generally open less widely under increased carbon dioxide concentration, which reduces transpiration and thus leaves more water at the land surface. This driver of change in the climate system, which we term 'physiological forcing', has been detected in observational records of increasing average continental runoff over the twentieth century. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11 +/- 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources.

Eleven coupled climate-carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850-2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1 degrees and 1.5 degrees C.All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

Modelling simulations of palaeoclimate and past vegetation form and function can contribute to global change research by constraining predictions of potential earth system responses to future warming, and by providing useful insights into the ecophysiological tolerances and threshold responses of plants to varying degrees of atmospheric change. We contrasted HadCM3LC simulations of Amazonian forest at the last glacial maximum (LGM; 21 kyr ago) and a Younger Dryas-like period (13-12 kyr ago) with predicted responses of future warming to provide estimates of the climatic limits under which the Amazon forest remains relatively stable. Our simulations indicate that despite lower atmospheric CO2 concentrations and increased aridity during the LGM, Amazonia remains mostly forested, and that the cooling climate of the Younger Dryas-like period in fact causes a trend toward increased above-ground carbon balance relative to today. The vegetation feedbacks responsible for maintaining forest integrity in past climates (i.e. decreased evapotranspiration and reduced plant respiration) cannot be maintained into the future. Although elevated atmospheric CO2 contributes to a positive enhancement of plant carbon and water balance, decreased stomatal conductance and increased plant and soil respiration cause a positive feedback that amplifies localized drying and climate warming. We speculate that the Amazonian forest is currently near its critical resiliency threshold, and that even minor climate warming may be sufficient to promote deleterious feedbacks on forest integrity.

Strategies to mitigate anthropogenic climate change recognize that carbon sequestration in the terrestrial biosphere can reduce the build-up of carbon dioxide in the Earth's atmosphere. However, climate mitigation policies do not generally incorporate the effects of these changes in the land surface on the surface albedo, the fluxes of sensible and latent heat to the atmosphere, and the distribution of energy within the climate system. Changes in these components of the surface energy budget can affect the local, regional, and global climate. Given the goal of mitigating climate change, it is important to consider all of the effects of changes in terrestrial vegetation and to work toward a better understanding of the full climate system. Acknowledging the importance of land surface change as a component of climate change makes it more challenging to create a system of credits and debits wherein emission or sequestration of carbon in the biosphere is equated with emission of carbon from fossil fuels. Recognition of the complexity of human-caused changes in climate does not, however, weaken the importance of actions that would seek to minimize our disturbance of the Earth's environmental system and that would reduce societal and ecological vulnerability to environmental change and variability. © 2003 Elsevier Science Ltd. All rights reserved.

Changes in land cover affect climate through the surface energy and moisture budgets, but these biogeophysical impacts of land use have not yet been included in General Circulation Model (GCM) simulations of 20th century climate change. Here, the importance of these effects was assessed by comparing climate simulations performed with current and potential natural vegetation. The northern mid-latitude agricultural regions were simulated to be approximately 1-2K cooler in winter and spring in comparison with their previously forested state, due to deforestation increasing the surface albedo by approximately 0.1 during periods of snow cover. Some other regions such as the Sahel and India experienced a small warming due to land use. Although the annual mean global temperature is only 0.02K lower in the simulation with present-day land use, the more local temperature changes in some regions are of a similar magnitude to those observed since 1860. The global mean radiative forcing by anthropogenic surface albedo change relative to the natural state is simulated to be -0.2Wm2, which is comparable with the estimated forcings relative to pre-industrial times by changes in stratospheric and tropospheric ozone, N2O, halocarbons, and the direct effect of anthropogenic aerosols. Since over half of global deforestation has occurred since 1860, simulations of climate since that date should include the biogeophysical effects of land use. © 2001 Royal Meteorological Society.

The rising atmospheric concentration of carbon dioxide resulting from the burning of fossil fuels and deforestation is likely to provoke significant climate perturbations, while having far-reaching consequences for the terrestrial biosphere. Some plants could maintain the same intake of CO2 for photosynthesis by reducing their stomatal openings, thus limiting the transpiration and providing a positive feedback to the projected surface warming. Other plants could benefit from the higher CO2 level and the warmer climate to increase their productivity, which would on the contrary promote the transpiration. The relevance of these feedbacks has been investigated with the Météo-France atmospheric general circulation model. The model has been run at the T31 spectral truncation with 19 vertical levels and is forced with sea surface temperature and sea ice anomalies provided by a transient simulation performed with the Hadley Centre coupled ocean-atmosphere model. Besides a reference doubled-CO2 experiment with no modification of the vegetation properties, two other experiments have been performed to explore the impact of changes in the physiology (stomatal resistance) and structure (leaf area index) of plants. Globally and annually averaged, the radiative impact of the CO2 doubling leads to a 2°C surface warming and a 6% precipitation increase, in keeping with previous similar experiments. The vegetation feedbacks do not greatly modify the model response on the global scale. The increase in stomatal resistance does not systematically lead to higher near-surface temperatures due to changes in the soil wetness annual cycle and the atmospheric circulation. However, both physiological and structural vegetation feedbacks are evident on the regional scale. They are liable to modify the CO2 impact on the hydrological cycle, as illustrated for the case of the European summertime climate and the Asian summer monsoon. The strong sensitivity of the climate in these areas emphasizes the large uncertainties of climate change predictions for some of the most populated regions of the world and argues for the need to include more interactive land surface processes in current generation climate models.

Carbon uptake by forestation is one method proposed to reduce net carbon dioxide emissions to the atmosphere and so limit the radiative forcing of climate change. But the overall impact of forestation on climate will also depend on other effects associated with the creation of new forests. In particular, the albedo of a forested landscape is generally lower than that of cultivated land, especially when snow is lying, and decreasing albedo exerts a positive radiative forcing on climate. Here I simulate the radiative forcings associated with changes in surface albedo as a result of forestation in temperate and boreal forest areas, and translate these forcings into equivalent changes in local carbon stock for comparison with estimated carbon sequestration potentials. I suggest that in many boreal forest areas, the positive forcing induced by decreases in albedo can offset the negative forcing that is expected from carbon sequestration. Some high-latitude forestation activities may therefore increase climate change, rather than mitigating it as intended.

Recent improvements to the Hadley Centre climate model include the introduction of a new land surface scheme called "MOSES" (Met Office Surface Exchange Scheme). MOSES is built on the previous scheme, but incorporates in addition an interactive plant photosynthesis and conductance module, and a new soil thermodynamics scheme which simulates the freezing and melting of soil water, and takes account of the dependence of soil thermal characteristics on the frozen and unfrozen components. The impact of these new features is demonstrated by comparing 1 x CO2 and 2 x CO2 climate simulations carried out using the old (UKMO) and new (MOSES) land surface schemes. MOSES is found to improve the simulation of current climate. Soil water freezing tends to warm the high-latitude land in the northern Hemisphere during autumn and winter, whilst the increased soil water availability in MOSES alleviates a spurious summer drying in the mid-latitudes. The interactive canopy conductance responds directly to CO2, suppressing transpiration as the concentration increases and producing a significant enhancement of the warming due to the radiative effects of CO2 alone.