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One of the COP26 aims was to keep 1.5°C within reach by asking countries to come forward with ambitious year 2030 emission reductions targets to further pursue the necessary action to meet the Paris climate targets. We assess the mean global temperature rise given the updated year 2030 emission pledges in the context of future emission pathways considered by the international scientific community. Overall, we find current pledges are not consistent with a likely meeting of 1.5°C this century without overshoot. Meeting the 1.5°C goal in 2100 post overshoot given the pledges remains feasible, but urgent action is required to ensure pledges are met and policies are in place for the very deep and rapid emission reductions that are required post 2030.

Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by the atmospheric models’ climate biases, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

We show likely substantial increases in burning by 2100 in Boreal and Tropical Forests irrespective of future emissions and after accounting for the (often considerable) uncertainties and biases in global fire and climate modelling. Rather than projecting future fire regimes directly, we used the ConFire Bayesian framework to model the likelihood of all possible future burning levels given historic fire and climate model performance. Driving the framework with bias-corrected outputs from four ISIMIP2b GCMs run under RCP2.6 and RCP6.0 accounts for uncertainties in future emissions and climate model projections. While we forecast the potential for substantial shifts in fire regimes of much of the world by the end of the century, many show low likelihood given our confidence in the fire, vegetation and climate model projections. Tropical savannas show the largest potential for change, though without confidence in the direction of change due to uncertainty in future precipitation projections. An increase in dry fuel drives an increase in burnt area in northern Australia. However, this is not significant against uncertainty associated with present-day veg/fire model performance. There is a significant agreement for decreased burning in Southern Brazil, Uruguay and northern Argentina, and the US east coast under RCP2.6, but not RCP6.0.We do show a high likelihood of drying fuel loads driving an increase in burning in Indonesia, Southern Amazon, central and eastern Siberian Taiga and many Arctic areas across RCPs. These areas are of particular concern given the potential to release the high carbon content of forests and peatlands irrecoverable carbon. Mitigating from RCP6.0 to 2.6 will likely alleviate some though not all of this burning. This is important for future mitigation planning and determining likely temperature and emission targets to avoid the worst impacts of fire in our warmer world.

The JULES land surface model has a wide ranging application in studying different processes of the earth system including hydrological modeling [1]. Our aim is to tune the existing configuration of the global river routing scheme at 0.5o spatial resolution [2] and improve river flow simulation performance at finer temporal scales. To do so, we develop a factorial experiment of varying effective river velocity and meander coefficient, components of the Total Runoff Integrating Pathways (TRIP) river routing scheme. We test and adjust best performing configurations at the basin scale based on observations from GRDC 230 stations that exhibiting a variety of hydroclimatic and physiographic conditions. The analysis was focused on watersheds of near-natural conditions [3] to avoid potential influences of human management on river flow. The HydroATLAS database [4] was employed to identify basin scale descriptive hydro-environmental indicators that could be associated with the components of the TRIP. These indicators summarize hydrologic and physiographic characteristics of the drainage area of each flow gauge. For each basin we select the best performing set of TRIP parameters per basin resulting to the optimal efficiency of river flow simulation based on the Nash–Sutcliffe and Kling–Gupta efficiency metrics. We find that better performance is driven predominantly by characteristics related to the stream gradient and terrain slope. These indicators can serve as descriptors for extrapolating the adjustment of TRIP parameters for global land configurations at 0.5o spatial resolution using regression models. [1] Papadimitriou et al 2017, Hydrol. Earth Syst. Sci., 21, 4379–4401[2] Falloon et al 2007. Hadley Centre Tech. Note 72, 42 pp.[3] Fang Zhao et al 2017 Environ. Res. Lett. 12 075003[4] Linke et al 2019, Scientific Data 6: 283.

Land-surface models (LSMs) typically simulate a single crop per year in a field or location. However, actual cropping systems are characterized by a succession of distinct crop cycles that are sometimes interspersed with long periods of bare soil. Sequential cropping (also known as multiple or double cropping) is particularly common in tropical regions, where the crop seasons are largely dictated by the main wet season. In this paper, we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at sites in France and India. We simulate all the crops grown within a year in a field or location in a seamless way to understand how sequential cropping influences the surface fluxes of a land-surface model. We evaluate JULES with sequential cropping in Avignon, France, providing over 15 years of continuous flux observations (a point simulation). We apply JULES with sequential cropping to simulate the rice–wheat rotation in a regional 25 km resolution gridded simulation for the northern Indian states of Uttar Pradesh and Bihar and four single-grid-box simulations across these states, where each simulation is a 25 km grid box. The inclusion of a secondary crop in JULES using the sequential cropping method presented does not change the crop growth or development of the primary crop. During the secondary crop growing period, the carbon and energy fluxes for Avignon and India are modified; they are largely unchanged for the primary crop growing period. For India, the inclusion of a secondary crop using this sequential cropping method affects the available soil moisture in the top 1.0 m throughout the year, with larger fluctuations in sequential crops compared with single-crop simulations even outside the secondary crop growing period. JULES simulates sequential cropping in Avignon, the four India locations and the regional run, representing both crops within one growing season in each of the crop rotations presented. This development is a step forward in the ability of JULES to simulate crops in tropical regions where this cropping system is already prevalent. It also provides the opportunity to assess the potential for other regions to implement sequential cropping as an adaptation to climate change.

Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments. We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled general circulation models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. The application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate.

Sequential cropping (also known as multiple or double cropping) is a common feature, particularly for tropical regions, where the crop seasons are largely dictated by the main wet season such as the Asian summer monsoon (ASM). The ASM provides the water resources for crops grown for the whole year, thereby influencing crop production outside the ASM period. Land surface models (LSMs) typically simulate a single crop per year, however, in order to understand how sequential cropping influences demand for resources, we need to simulate all of the crops grown within a year in a seamless way. In this paper we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at Avignon, a site that uses the sequential cropping system and provides over 15-years of continuous flux observations which we use to evaluate JULES with sequential cropping. In order to implement the method in future regional simulations where there may be large variations in growing conditions, we apply the same method to four locations in the North Indian states of Uttar Pradesh and Bihar to simulate the rice--wheat rotation and compare model yields to observations at these locations. JULES is able to simulate sequential cropping at Avignon and the four India locations, representing both crops within one growing season in each of the crop rotations presented. At Avignon the maxima of LAI, above ground biomass and canopy height occur at approximately the correct time for both crops. The magnitudes of biomass, especially for winter wheat, are underestimated and the leaf area index is overestimated. The JULES fluxes are a good fit to observations (r-values greater than 0.7), either using grasses to represent crops or the crop model, implying that both approaches represent the surface coverage correctly. For the India simulations, JULES successfully reproduces observed yields for the eastern locations, however yields are under estimated for the western locations. This development is a step forward in the ability of JULES to simulate crops in tropical regions, where this cropping system is already prevalent, while also providing the opportunity to assess the potential for other regions to implement it as an adaptation to climate change.

The Nile Basin is one of the most important shared basins in Africa. Managing and developing the water resources within the basin must not only address different water uses but also the trade-off between developments upstream and water use downstream, often between different countries. Furthermore, decision-makers in the region need to evaluate and implement climate adaptation measures. Previous work has shown that the Nile flows can be highly sensitive to climate change and that there is considerable uncertainty in climate projections in the region with no clear consensus as to the direction of change. Modelling current and future changes in river runoff must address a number of challenges; including the large size of the basin, the relative scarcity of data, and the corresponding dramatic variety of climatic conditions and diversity in hydrological characteristics. In this paper, we present a methodology, to support climate adaptation on a regional scale, for assessing climate change impacts and adaptation potential for floods, droughts and water scarcity within the basin.

Sowing and harvest dates are a significant source of uncertainty within crop models especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and sub-tropical regions, they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomena, the Asian Summer Monsoon (ASM). Rice and wheat are two highly important crops for the region, rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates, for rice and wheat, using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than are currently available and generate input for climate impact assessments. We first demonstrate that there is skill in the model prediction of monsoon onset and retreat for two downscaled General Circulation Models (GCMs) by comparing modelled precipitation with observations. We then calculate and apply sowing and harvest rules for rice and wheat for each simulation to climatological estimates of the monsoon onset and retreat for a present day period. We show that this method reproduces the present day sowing and harvest dates for most parts of India. Application of the method to two future simulations demonstrates that the estimated sowing and harvest dates are successfully modified to ensure that the growing season remains consistent with the internal model climate. The study therefore provides a useful way of modelling potential growing season adaptations to changes in future climate.

The globally averaged mass balance of glaciers and ice caps is negative, but an anomalous gain of mass has been suggested for the Karakoram glaciers of the western Himalaya. Changes in the winter synoptic patterns can influence the amount and seasonality of Himalayan snowfall and consequently influence the mass balance of regional glaciers. We use a clustering method to analyse the sea level pressure patterns which most influence Karakorum snow fall and determine if the frequency of these synoptic patterns changes in future scenarios. A regional climate model is used to assess changes in severity and frequency of snowfall events. A number of weather patterns influence winter precipitation over the western Himalaya, including westerly disturbances, and indicate an increase in frequency of occurrence up to 2100. Thus, the Karakorum glaciers may continue to grow, or decline at a slower rate, compared with those across the rest of the Himalayas.

Great Britain’s main line railway network is known to experience various temperature-related impacts, e.g. track buckling and overhead power line sag at high ambient temperatures. Climate change could alter the frequency of occurrence of these impacts. We have therefore investigated the climate change impact on various temperature-related issues, identified during workshops with rail industry specialists, using a perturbed physics ensemble (PPE) of the Met Office’s regional climate model (RCM), HadRM3. We have developed novel approaches to combine RCM data with railway industry knowledge, typically by identifying key meteorological thresholds of interest and analysing exceedance of these out to the 2040s. We performed a statistical analysis of the projected changes for each issue, via bootstrapping of the unperturbed PPE member. Although neither the PPE nor the bootstrapping analysis samples the full range of uncertainty in the projections, they nonetheless provide complementary perspectives on the suitability of the projections for use in decision-making. Our main findings include projected increases in the summertime occurrence of temperature conditions associated with (i) track buckling, (ii) overhead power line sag, (iii) exposure of outdoor workers to heat stress, and (iv) heat-related delays to track maintenance; and (v) projected decreases in the wintertime occurrence of temperatures conditions associated with freight train failure owing to brake problems. For (i), the statistical significance varied with track condition and location; for (ii) and (iii), with location; and for (iv) and (v), projected changes were significant across Great Britain. As well as assessing the changes in climate-related hazard, information about the vulnerability of the network to past temperature-related incidents has been summarised. Combining the hazard and vulnerability elements will eventually support a climate risk assessment for the industry.

The accuracy of Met Office middle atmospheric analyses is investigated via direct validation against observational temperature data from two independent satellite instruments. A climatology of monthly zonal mean temperature biases from January 2005 to December 2010 is presented. For analyses produced before October 2009 there is a consistent cold bias of ∼30–40 K in the polar winter lower mesosphere, suggestive that gravity wave forcing in this region is too weak. Such cold biases are accompanied by smaller-magnitude cold biases in the opposing summer hemisphere, most likely associated with compensating warm biases from an insufficient gravity wave-driven circulation and cold biases resulting from deficiencies in radiative heating due to the operational ozone climatology. For analyses produced after October 2009, where the model lid was raised to include the upper mesosphere, cold biases in the winter lower mesosphere are reduced. However, systematic warm biases of ∼40–60 K in the summer upper mesosphere again suggest that gravity wave forcing within the operational system is underestimated. The absence of cold biases in the winter upper mesosphere also suggests that radiative errors contribute significantly in this region.

South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

This study presents the possible regional climate change over South Asia with a focus over India as simulated by three very high resolution regional climate models (RCMs). One of the most striking results is a robust increase in monsoon precipitation by the end of the 21st century but regional differences in strength. First the ability of RCMs to simulate the monsoon climate is analyzed. For this purpose all three RCMs are forced with ECMWF reanalysis data for the period 1989-2008 at a horizontal resolution of ~25 km. The results are compared against independent observations. In order to simulate future climate the models are driven by lateral boundary conditions from two global climate models (GCMs: ECHAM5-MPIOM and HadCM3) using the SRES A1B scenario, except for one RCM, which only used data from one GCM. The results are presented for the full transient simulation period 1970-2099 and also for several time slices. The analysis concentrates on precipitation and temperature over land. All models show a clear signal of gradually wide-spread warming throughout the 21st century. The ensemble-mean warming over India is 1.5°C at the end of 2050, whereas it is 3.9°C at the end of century with respect to 1970-1999. The pattern of projected precipitation changes shows considerable spatial variability, with an increase in precipitation over the peninsular of India and coastal areas and, either no change or decrease further inland. From the analysis of a larger ensemble of global climate models using the A1B scenario a wide spread warming (~3.2°C) and an overall increase (~8.5%) in mean monsoon precipitation by the end of the 21st century is very likely. The influence of the driving GCM on the projected precipitation change simulated with each RCM is as strong as the variability among the RCMs driven with one.

Adaptation is increasingly important for regions around the world where large changes in climate could have an impact on populations and industry. The Brahmaputra–Ganges catchments have a large population, a main industry of agriculture and a growing hydro-power industry, making the region susceptible to changes in the Indian Summer Monsoon, annually the main water source. The HighNoon project has completed four regional climate model simulations for India and the Himalaya at high resolution (25 km) from 1960 to 2100 to provide an ensemble of simulations for the region. In this paper we have assessed the ensemble for these catchments, comparing the simulations with observations, to give credence that the simulations provide a realistic representation of atmospheric processes and therefore future climate. We have illustrated how these simulations could be used to provide information on potential future climate impacts and therefore aid decision-making using climatology and threshold analysis. The ensemble analysis shows an increase in temperature between the baseline (1970–2000) and the 2050s (2040–2070) of between 2 and 4 °C and an increase in the number of days with maximum temperatures above 28 °C and 35 °C. There is less certainty for precipitation and runoff which show considerable variability, even in this relatively small ensemble, spanning zero. The HighNoon ensemble is the most complete data for the region providing useful information on a wide range of variables for the regional climate of the Brahmaputra–Ganges region, however there are processes not yet included in the models that could have an impact on the simulations of future climate. We have discussed these processes and show that the range from the HighNoon ensemble is similar in magnitude to potential changes in projections where these processes are included. Therefore strategies for adaptation must be robust and flexible allowing for advances in the science and natural environmental changes.

The Himalayan region is characterized by pronounced topographic heterogeneity and land use variability from west to east, with a large variation in regional climate patterns. Over the western part of the region, almost one-third of the annual precipitation is received in winter during cyclonic storms embedded in westerlies, known locally as the western disturbance. In the present paper, the regional winter climate over the western Himalayas is analyzed from simulations produced by two regional climate models (RCMs) forced with large-scale fields from ERA-Interim. The analysis was conducted by the composition of contrasting (wet and dry) winter precipitation years. The findings showed that RCMs could simulate the regional climate of the western Himalayas and represent the atmospheric circulation during extreme precipitation years in accordance with observations. The results suggest the important role of topography in moisture fluxes, transport and vertical flows. Dynamical downscaling with RCMs represented regional climates at the mountain or even event scale. However, uncertainties of precipitation scale and liquid-solid precipitation ratios within RCMs are still large for the purposes of hydrological and glaciological studies. © 2013 Elsevier B.V.

An ensemble of regional climate model (RCM) runs from the EU HighNoon project are used to project future air temperatures and precipitation on a 25 km grid for the Ganges basin in northern India, with a view to assessing impact of climate change on water resources and determining what multi-sector adaptation measures and policies might be adopted at different spatial scales. The RCM results suggest an increase in mean annual temperature, averaged over the Ganges basin, in the range 1–4 °C over the period from 2000 to 2050, using the SRES A1B forcing scenario. Projections of precipitation indicate that natural variability dominates the climate change signal and there is considerable uncertainty concerning change in regional annual mean precipitation by 2050. The RCMs do suggest an increase in annual mean precipitation in this region to 2050, but lack significant trend. Glaciers in headwater tributary basins of the Ganges appear to be continuing to decline but it is not clear whether meltwater runoff continues to increase. The predicted changes in precipitation and temperature will probably not lead to significant increase in water availability to 2050, but the timing of runoff from snowmelt will likely occur earlier in spring and summer. Water availability is subject to decadal variability, with much uncertainty in the contribution from climate change. Although global social-economic scenarios show trends to urbanization, locally these trends are less evident and in some districts rural population is increasing. Falling groundwater levels in the Ganges plain may prevent expansion of irrigated areas for food supply. Changes in socio-economic development in combination with projected changes in timing of runoff outside the monsoon period will make difficult choices for water managers. Because of the uncertainty in future water availability trends, decreasing vulnerability by augmenting resilience is the preferred way to adapt to climate change. Adaptive policies are required to increase society's capacity to adapt to both anticipated and unanticipated conditions. Integrated solutions are needed, consistent at various spatial scales, to assure robust and sustainable future use of resources. For water resources this is at the river basin scale. At present adaptation measures in India are planned at national and state level, not taking into account the physical boundaries of water systems. To increase resilience adaptation plans should be made locally specific. However, as it is expected that the partitioning of water over the different sectors and regions will be the biggest constraint, a consistent water use plan at catchment and river basin scale may be the best solution. A policy enabling such river basin planning is essential.

The formulation and performance of the Met Office visibility analysis and prediction system are described. The visibility diagnostic within the limited-area Unified Model is a function of humidity and a prognostic aerosol content. The aerosol model includes advection, industrial and general urban sources, plus boundary-layer mixing and removal by rain. The assimilation is a 3-dimensional variational scheme in which the visibility observation operator is a very nonlinear function of humidity, aerosol and temperature. A quality control scheme for visibility data is included. Visibility observations can give rise to humidity increments of significant magnitude compared with the direct impact of humidity observations. We present the results of sensitivity studies which show the contribution of different components of the system to improved skill in visibility forecasts. Visibility assimilation is most important within the first 6-12 hours of the forecast and for visibilities below 1 km, while modelling of aerosol sources and advection is important for slightly higher visibilities (1-5 km) and is still significant at longer forecast times

The Met Office Hadley Centre’s PRECIS regional climate modelling system has been used to generate a five member ensemble of climate projections for Africa over the 50 km resolution Coordinated Regional climate Downscaling Experiment-Africa domain. The ensemble comprises the downscaling of a subset of the Hadley Centre’s perturbed physics global climate model (GCM) ensemble chosen to exclude ensemble members unable to represent the African climate realistically and then to capture the spread in outcomes from the projections of the remaining models. The PRECIS simulations were run from December 1949 to December 2100. The regional climate model (RCM) ensemble captures the annual cycle of temperatures well both for Africa as a whole and the sub-regions. It slightly overestimates precipitation over Africa as a whole and captures the annual cycle of rainfall for most of the African regions. The RCM ensemble substantially improve the patterns and magnitude of precipitation simulation compared to their driving GCM which is particularly noticeable in the Sahel for both the magnitude and timing of the wet season. Present-day simulations of the RCM ensemble are more similar to each other than those of the driving GCM ensemble which indicates that their climatologies are influenced significantly by the RCM formulation and less so by their driving GCMs. Consistent with this, the spread and magnitudes of the large-scale responses of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.