Regional Climate Change Assessment Research Articles

Is interactive air sea coupling relevant for simulating the future climate of Europe?

The majority of regional climate change assessments for the Euro-CORDEX region is based on high resolution atmosphere models. These models use prescribed lower boundary conditions, such as sea surface temperatures (SST) from global ocean General Circulation Models (GCMs), that do not respond to changes simulated by the regional atmosphere model, thus lacking an important feedback to the atmosphere. However, research during the past decade indicated that the use of coupled atmosphere–ocean models can lead to significantly altered model solutions compared to standalone atmosphere models for the present day climate imposing some uncertainty on the widely used uncoupled future scenarios. We here present the first multi-model and multi scenario (RCP2.6, RCP4.5, RCP8.5) ensemble of future climate change scenarios downscaled with a coupled atmosphere—ocean model in which sea surface temperature and sea ice fields are explicitly simulated by a coupled state-of-the-art high resolution ocean model and communicated to the atmosphere at 3-hourly time steps. Our ensemble generally confirms results of previous uncoupled ensembles over land areas implying that the coupling effect is restricted mainly to the coupled area and the adjacent coastal zone. By contrast, over the North Sea and Baltic Sea small scale processes point to important coupling effects that mediate the response to climate change and that can not be simulated by uncoupled models. Our results therefore impose general uncertainty on the usage of regional climate change data from uncoupled ensembles over marine areas such as for purposes of offshore wind or mussel farming, the planing of marine protected areas, and marine recreation along the coastal zone. It further sets in question the usage of uncoupled scenario data (such as Euro-CORDEX) to force high resolution ocean models. Comparing coupled and uncoupled hindcast simulations reveals that the coupling effect over land is most pronounced during the warm season when prescribed and modelled sea surface temperatures (SST) differ strongest. In addition, a generally weaker wind regime in summer damps the heat dispersion in the atmosphere so that air temperature anomalies can extent further over land compared to winter. Future projections are discussed under consideration of land-sea warming characteristics for selected climate indices as well as mean seasonal climate change. At the end of the century a clear land-sea pattern is seen in all scenarios with stronger warming over land than over open sea areas. On average land areas warm at a rate 1.5 times faster than areas over the open ocean. Over the coupled area, i.e. the North Sea and Baltic Sea tropical nights are impacted strongest and the Baltic Sea turns out to be a hot spot in future climate. This has been unrecognized in previous studies using high resolution atmosphere models with prescribed SSTs from global models which do not represent small scale ocean processes in the Baltic Sea adequately.

Modeling climate change impact on streamflow as affected by snowmelt in Nicolet River Watershed, Quebec

Frequent spring flooding in Southern Quebec’s Nicolet River watershed has a history of causing severe damage, which is likely to worsen as climate change progresses. Employing the ArcSWAT model, an attempt was made to assess the potential impacts of climate change on the Nicolet River watershed’s seasonal and annual streamflow, particularly that portion affected by snowmelt. Calibrated and validated against observed streamflow data for the periods of 1986–1990 and 1991–2000, respectively, the model reliably predicted daily streamflow (e.g., percent bias within ±15%, Nash-Sutcliffe model efficiency >0.50, and the ratio of root mean square error to the standard deviation ≤0.70). In an effort to investigate the impacts of climate change on streamflow, future climate datasets were generated for 2053–2067 by implementing the eleven sets of existing Regional Climate Model (RCM) simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP) in the ArcSWAT model. The ArcSWAT model’s hydrological responses were closely tied to changes of climate variables: a strong correlation existed between simulated runoff and precipitation, and between temperature and predicted evapotranspiration, snowfall, and winter snowmelt. Projected future climate data showed increases in both average temperature (+2.5 °C) and precipitation (+21%). Significant greater total precipitation was forecasted for the winter season, while total snowfall was projected to decrease by 6%. However, the snowmelt showed an increasing trend for the late winter and earlier spring period. Streamflow was expected to increase annually and in most seasons except spring. Annual peak flows volumes would increase by 13% in the future and the occurrence of peak flows would shift to the winter (vs. the spring), indicating a greater risk of winter flooding in the future. The individual impact of temperature and precipitation on peak flows showed that increases in peak flows were mainly tied to increased precipitation, while the shift in their timing was mostly tied to warming temperatures.

A Future of Retreating Glaciers in the Himalayas

India’s first regional climate change assessment warns of accelerated glacier melt.

Mid-21st century anthropogenic changes in extreme precipitation and snowpack projections over Newfoundland

Extreme precipitation events, including probable maximum precipitation (PMP) and probable maximum snow accumulation (PMSA) and 1/100 annual exceedance probability (AEP) values for precipitation (P100) and snow accumulation (expressed in snow water equivalent; SWE100) were analyzed over Newfoundland to compute the projected changes from 1971–2000 to 2041–2070. PMP and PMSA of various storm durations were simulated based on the moisture maximization of high efficiency storms. Also, P100 and SWE100 data were calculated based on the frequency analysis of liquid precipitation and snowpack data during each 30-year period. The required meteorological variables, including liquid and solid precipitation, precipitable water content, and snow accumulation, defined over a 50 × 50 km grid, were extracted from an ensemble of six regional climate model simulations provided by the North American Regional Climate Change Assessment Program (NARCCAP). Projections indicated that while PMP and P100 are intensifying in the future period, PMSA and SWE100 are declining. This is the first study which quantifies the impact of climate change on extreme-value characteristics of precipitation in Newfoundland. The results of the study can help stakeholders throughout the province to gain a better understanding of the impact of global warming on extreme meteorological events. Such an understanding is prerequisite to build resiliency and understand the uncertainty related to standard probable maximum flood analysis in the region.

The impacts of a warming climate on winter mid-latitude cyclones in the NARCCAP model suite

The North American Regional Climate Change Assessment Program (NARCCAP) represents the next step in investigating the behavior of weather phenomena in current and future climate. Specifically, variations in mid-latitude cyclone tracks attributable to a warming climate have potential socio-economic consequences via the redistribution of precipitation on regional spatial scales. This manuscript assesses the impacts of a warming climate on cyclone tracks in the NARCCAP model suite. Specifically, cyclones are generated from eight 33-year simulations for the twentieth Century (1968–2000) and eight 33-year simulations for the A2 greenhouse scenario (2068–2100). To provide comparison, cyclone tracks are also generated from the Climate Forecast System Reanalysis (CFSR) and ERA Interim (ERA_INT) reanalysis datasets from 1979 to 2016. The results support that the NARCCAP model suite is capable of producing a reasonable cyclone frequency and intensity climatology when compared with the reanalysis datasets. Comparison of the ensemble twentieth Century cyclone (20C) tracks with the ensemble A2 cyclone tracks demonstrate a zonally-oriented poleward shift in cyclone track frequency in response to a warming climate. Intensity differences were regionally oriented, with cyclones being more intense in the A2 relative to the twentieth Century scenarios west of the Appalachians, suggesting cyclones acquire greater latent heat from warmer western Atlantic/Gulf of Mexico moisture sources in A2. A sector analysis revealed a higher total frequency of cyclones in both reanalysis datasets relative to either NARCCAP scenario. For intense cyclones, the NARCCAP model simulations produced more frequent cyclones in the Great Lakes and East Coast sectors. In contrast, reanalysis produced a higher frequency of weak cyclones for all sectors except Atlantic Canada. In addition, NARCCAP simulations were found to be capable of reproducing the broadness of intensity distributions in the reanalysis datasets, likely due to the fine spatial gridding in the NARCCAP models. Sector analysis for frequency and intensity affirmed the ensemble results, with individual model simulations showed a reduction in frequency for all sectors except the Canadian Maritimes for A2 relative to 20C. For intensity, cyclones were once again overall more intense for the upper Midwest/Great Lakes sector for A2 relative to 20C. The results of this research demonstrates the ability for regional climate models to be used to assess changes in synoptic-scale phenomena in a warming climate. Future work will focus on assessing cyclone structure including changes in moisture transport, precipitation, and the low-level jet.

AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).

Opposite spatial variability of climate change‐induced surface temperature trends due to soil and atmospheric moisture in tropical/subtropical dry and wet land regions

AbstractRegional responses to surface warming are strongly influenced by the availability of surface or atmospheric moisture. For example, differences in evapotranspiration, atmospheric temperature lapse rate and heat capacity due to the availability of water over oceans and its lack over land contribute to an observed land/ocean warming ratio that is greater than one. This study shows that mechanisms related to the distributions of soil or atmospheric moisture could result in variations in regional climate change over land in the tropics and subtropics. Using atmospheric reanalyses and observations for the satellite era (1979–2016) within ±30° latitude, the analysis shows that dry regions such as northern and southern Africa and south‐central Australia have a significant negative regional spatial correlation between time‐averaged soil moisture/evapotranspiration and surface temperature trends. In contrast, wet tropical regions such as Amazonia and the Congo Basin display a significant positive spatial correlation between these quantities. Spatial correlations suggest that a regional amplification of surface temperature trends in wet regions could be associated with a robust positive longwave radiative effect of high clouds which co‐occur with wet soils. This positive relationship is strongest during austral summer, dominated by the summer seasons in the Amazon and Congo basins. These differences in regional sensitivity to greenhouse gas warming can be consequential for regional climate change assessment, for both tropical forests, which could already be operating at their high temperature limit, and also for dry desert regions, which are already marginal for supporting life.

Evaluation of CORDEX-RCMS and their driving GCMs of CMIP5 in simulation of Indian summer monsoon rainfall and its future projections

Climate models are widely used for global and regional assessment of climate change. The present study aims to assess the ability of regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) and their driving global climate models (GCMs) of Coupled Models Intercomparison Project phase 5 (CMIP5) in simulating the Indian summer monsoon rainfall (ISMR) over India (1979–2005). The assessment of CORDEX-RCMs driven by the boundary conditions from GCMs is necessary to know the capability of RCMs in the simulation of ISMR over India. The spatiotemporal analysis of ISMR is performed for past periods to understand its characteristics while attempt is made for future projected changes over the homogeneous rainfall region of India. The study reveals that RCM REMO2009 (MPI) and its driving GCM MPI-ESM-LR are close to the observed rainfall of Global Precipitation Climatology Centre (GPCC). Further, it is noticed that the biases in REMO2009 (MPI) and GCMs viz. MPI-ESM-LR and GFDL-CM3 are of comparable amplitude making them suitable for future projection of ISMR for 2016–2045 under Representative Concentration Pathways (RCPs) 4.5 and 8.5 at 99% and 95% confidence levels. In the simulation of REMO2009 (MPI) and its driving GCM (MPI-ESM-LR) under RCPs 4.5 and 8.5, an excess of rainfall is possible over the parts of Peninsular India (PI) and West Central India (WCI) while a deficit over the North West India (NWI). The simulation of the GCM, GFDL-CM3 depicts an excess of rainfall over NWI, PI, and deficit over WCI under both emission scenarios.

Vulnerability and risk: climate change and water supply from California’s Central Valley water system

Water allocation institutions globally must operate within legal and political contexts established by precedent and codified in operating rules, even as they flex and adjust to climate change. California’s Central Valley Water System (CVS) is a prime example. Recent global, national, regional, and local climate change assessments have highlighted climate-change-driven impacts on the CVS; however, these previous studies have not discussed the relative likelihood of performance decline, making it difficult to use the information for planning. In response, this paper presents a systematic climate change stress test that utilizes a physically based hydrologic model linked with a water resources system model representing the infrastructure, operations, and policy constraints of the interconnected system of natural river channels and man-made facilities that comprise the CVS. The results provide a summary of the sensitivity of the system to climate change, indicating the specific climate changes that cause performance of the system to decline below historical norms, and an estimation of the General Circulation Model (GCM) informed probability of those changes by 2050. Degraded performance is especially likely for State Water Project (SWP) deliveries (> 85%), and September carryover/drought storage in the Oroville Reservoir (the SWP’s largest reservoir, ~ 95% likely to degrade). A decline in Net Delta Outflow is likely in all seasons except summer and early fall (when regulations require supplemental releases to combat salinity from sea level rise). For most of these metrics, the modeled performance drop is more severe in dry years than in wet years.

Incorporating Climate Change Projections into Risk Measures of Index-Based Insurance

We use regional climate model projections to quantify long-term projected changes to risk measures for an example set of temperature index–based insurance products in California. This region is a major agricultural producer for the United States and the world. The climate model projections are an ensemble of six regional climate model runs obtained from the North American Regional Climate Change Assessment Program. Hindcasts for the period of 1971–2000 are compared to historical observed temperature data for bias and variance corrections. Adjusted future model projections are used to estimate distributions of cooling degree days for 2041–2070, which are then used to estimate risk measures for index-based insurance products to demonstrate the scale of changes that climate models project through mid-century in actuarial terms. More broadly, this article provides an illustration of the use of climate data products to explore actuarial risks.

Synthesizing and communicating climate change impacts to inform coastal adaptation planning

Planning for adaptation to climate change requires regionally relevant information on rising air and ocean temperatures, sea levels, increasingly frequent and intense storms, and other climate-related impacts. However, in many regions there are limited focused syntheses of the climate impacts, risks, and potential adaptation strategies for coastal marine areas and sectors. We report on a regional assessment of climate change impacts and recommendations for adaptation strategies in the NE Pacific Coast (British Columbia, Canada), conducted in collaboration with a regional planning and plan implementation partnership (Marine Plan Partnership for the North Pacific Coast), aimed at bridging the gaps between climate science and regional adaptation planning. We incorporated both social and ecological aspects of climate change impacts and adaptations, and the feedback mechanisms which may result in both increased risks and opportunities for the following areas of interest: “Ecosystems”, “Fisheries and Aquaculture”, “Communities”, and “Marine Infrastructure”. As next steps within the region, we propose proactive planning measures including communication of the key impacts and projections and cross-sectoral assessments of climate vulnerability and risk to direct decision-making.

Climate Change Assessment Using Negative Extreme Deviations of Surface Temperature Field (Case Study: Tbilisi)

To conduct regional climate change assessment for the selected location (Tbilisi) more than century period hydrometeorological observation data of day-night minimal temperature is used. In the presented paper only temperature negative values of whole winter season are used. The temperature field change is characterized by the following four parameters: the minimal temperature sum (winter-day temperature is less than zero) of winter frost days; frost day number; season minimal temperature and the occurrence date of season minimal temperature. The modular structure of the above listed parameters has been studied and the mathematical expressions for temporal changes of those parameters have been obtained considering dynamical norms and cyclical changes. The intensity of this change in terms of global warming has been determined. One of the main parameters determining climate change is the variations of temperature field. Despite the fact that this parameter has been modified in the large range due to numerous impacts, they kept stable and provide sustainable equilibrium of the Earth’s energy potential during long-term (several decades) period. This was the reason why the Earth climate remained unchained. As it is known, the anthropogenic impact on the environment has breached the sustainable balance of the Earth energy potential, the potential has been gradually grown almost for more than a century and is known by the name of global warming. The above-mentioned process has already created the greatest threat to the existence of Earth’s biosphere and if it still continues the catastrophic results eventually have to be expected. This led that the problem solving has been set as an urgent task. The numerous fundamental studies for all regions of the world have been carried out to assess the intensity of this process.

Evaluation of the Climate Extremes Index over the United States using 20th and mid‐21st century North American Regional Climate Change Assessment Program data

AbstractMuch of our current risk assessment, especially for extreme events and natural disasters, comes from the assumption that the likelihood of future extreme events can be predicted based on the past. However, as global temperatures rise, established climate ranges may no longer be applicable, as historic records for extremes such as heat waves and floods may no longer accurately predict the changing future climate. To assess extremes (present‐day and future) over the contiguous United States, we used NOAA's Climate Extremes Index (CEI), which evaluates extremes in maximum and minimum temperature, extreme one‐day precipitation, days without precipitation, and the Palmer Drought Severity Index (PDSI). The CEI is a spatially sensitive index that uses percentile‐based thresholds rather than absolute values to determine climate “extremeness” and is thus well‐suited to compare extreme climate across regions. We used regional climate model data from the North American Regional Climate Change Assessment Program (NARCCAP) to compare a late 20th century reference period to a mid‐21st century “business as usual” (SRES A2) greenhouse gas‐forcing scenario. Results show a universal increase in extreme hot temperatures across all models, with annual average maximum and minimum temperatures exceeding 90th percentile thresholds consistently across the continental United States. Results for precipitation indicators have greater spatial variability from model to model, but indicate an overall movement towards less frequent but more extreme precipitation days in the future. Due to this difference in response between temperature and precipitation, the mid‐21st century CEI is primarily an index of temperature extremes, with 90th percentile temperatures contributing disproportionately to the overall increase in climate extremeness. We also examine the efficacy of the PDSI in this context in comparison to other drought indices.

IMPACTS OF CLIMATE CHANGE ON IRRIGATED AGRICULTURE IN THE CHALIYAR RIVER BASIN IN KERALA, INDIA

Impact assessment of regional climate change is very important as change in climate has emerged as one of the major threats to water resource systems and would significantly affect streamflow, soil moisture and water availability. The study used output of the Regional Climate Model (RCM) Remo2009 (Max-Planck-Institute (MPI)) to analyze the potential impacts of climate change on irrigated agriculture in the Chaliyar river basin, India. Streamflow and evapotranspiration were simulated using validated Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model. The estimation of irrigation water requirement (IWR) was performed using Food and Agriculture Organization (FAO) method for the period 2021–2030 and 2051–2060. Results show that projected streamflow increases during June to September and decreases during October to December and January to May in future. Crop water requirement and IWR showed an increase during dry season and decrease during wet season. The increase/decrease in streamflow and IWR during wet/dry season is more in the far future than near future and for RCP 8.5 scenario than RCP 4.5 scenario.

Extreme Precipitation Spatial Analog: In Search of an Alternative Approach for Future Extreme Precipitation in Urban Hydrological Studies

In this paper, extreme precipitation spatial analog is examined as an alternative method to adapt extreme precipitation projections for use in urban hydrological studies. The idea for this method is that real climate records from some cities can serve as “analogs” that behave like potential future precipitation for other locations at small spatio-temporal scales. Extreme precipitation frequency quantiles of a 3.16 km 2 catchment in the Chicago area, computed using simulations from North American Regional Climate Change Assessment Program (NARCCAP) Regional Climate Models (RCMs) with L-moment method, were compared to National Oceanic and Atmospheric Administration (NOAA) Atlas 14 (NA14) quantiles at other cities. Variances in raw NARCCAP historical quantiles from different combinations of RCMs, General Circulation Models (GCMs), and remapping methods are much larger than those in NA14. The performance for NARCCAP quantiles tend to depend more on the RCMs than the GCMs, especially at durations less than 24-h. The uncertainties in bias-corrected future quantiles of NARCCAP are still large compared to those of NA14, and increase with rainfall duration. Results show that future 3-h and 30-day rainfall in Chicago will be similar to historical rainfall from Memphis, TN and Springfield, IL, respectively. This indicates that the spatial analog is potentially useful, but highlights the fact that the analogs may depend on the duration of the rainfall of interest.

Interpreting Results from the NARCCAP and NA-CORDEX Ensembles in the Context of Uncertainty in Regional Climate Change Projections

AbstractTwo ensembles of dynamically downscaled climate simulations for North America—the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Climate Downscaling Experiment (CORDEX) featuring simulations for North America (NA-CORDEX)—are analyzed to assess the impact of using a small set of global general circulation models (GCMs) and regional climate models (RCMs) on representing uncertainty in regional projections. Selecting GCMs for downscaling based on their equilibrium climate sensitivities is a reasonable strategy, but there are regions where the uncertainty is not fully captured. For instance, the six NA-CORDEX GCMs fail to span the full ranges produced by models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) in summer temperature projections in the western and winter precipitation projections in the eastern United States. Similarly, the four NARCCAP GCMs are overall poor at spanning the full CMIP3 ranges in seasonal temperatures. For the Southeast, the NA-CORDEX GCMs capture the uncertainty in summer but not in winter projections, highlighting one consequence of downscaling a subset of GCMs. Ranges produced by the RCMs are often wider than their driving GCMs but are sensitive to the experimental design. For example, the downscaled projections of summer precipitation are of opposite polarity in two RCM ensembles in some regions. Additionally, the ability of the RCMs to simulate observed temperature trends is affected by the internal variability characteristics of both the RCMs and driving GCMs, and is not systematically related to their historical performance. This has implications for adequately sampling the impact of internal variability on regional trends and for using model performance to identify credible projections. These findings suggest that a multimodel perspective on uncertainties in regional projections is integral to the interpretation of RCM results.

Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data

Heat waves and other extreme weather events have attracted a great deal of attention due to their socioeconomic impacts and relation to climate change. A heat wave is defined through a general loss function that captures its amplitude, temporal persistence, and spatial extent. The proposed statistical framework is at the nexus of extreme value theory (EVT) and functional data analysis (FDA) and enables computation of probabilities of yet unobserved rare events that are not seen in historical records. Data from the North American Regional Climate Change Assessment Program, which has produced computer model predictions of current and future temperatures across much of North America, are used. The approach allows for the computation of probabilities for heat waves of any pre-specified temporal duration, spatial extent, and overall magnitude. It can be applied to the computation of probabilities of other extreme weather events, including cold spells and droughts.

Pacific sea surface temperature related influences on North American monsoon precipitation within North American Regional Climate Change Assessment Program models

Climate inter‐annual variability over the North American monsoon (NAM) region is associated with El Niño‐Southern Oscillation (ENSO) and Pacific decadal variability (PDV), which drive a warm season atmospheric teleconnection response. Using the North American Regional Climate Change Assessment Program (NARCCAP) simulations, previous studies have found that regional models forced with an atmospheric reanalysis (NARCCAP Phase I) represent the NAM reasonably well as a climatological feature. However, when these same regional models are forced with global climate model projections (NARCCAP Phase II), their ability to represent the NAM as a salient feature substantially degrades. The present study evaluates NAM inter‐annual climate variability through the continental‐scale patterns of summer precipitation within the NARCCAP simulations (Phases I and II), in relation to ENSO–PDV, and the presence of the driving atmospheric teleconnection response. Multivariate statistical analyses are applied to sea surface temperature and precipitation data sets to determine dominant variability at continental scale, with focus on the southwest. The analysis reveals that NARCCAP Phase I simulations are able to portray the spatial pattern of precipitation associated with ENSO–PDV in a similar way to observations. However, all NARCCAP Phase II simulations, with the exception of the HRM(Hadcm3) regional–global model pair, fail to reproduce this climate variability. Although including all possible NARCCAP model simulations to generate a multi‐model ensemble mean would increase the statistical degree of confidence in climate projections, this type of result would not increase confidence in the physical climatology of model representations of warm season climate variability. More physically based, process‐oriented metrics are needed to evaluate model quality in assessing the uncertainty of future climate change in multi‐model ensemble products used for climate change impacts assessments.

Bias patterns and climate change signals in GCM-RCM model chains

The assessment of regional climate change and the associated planning of adaptation and response strategies are often based on complex model chains. Typically these employ global and regional climate models (GCMs and RCMs), as well as one or several impact models. It is a common belief that the errors in such model chains behave approximately additive, thus the uncertainty should increase with each modeling step. If this hypothesis was true, the application of RCMs would not lead to any intrinsic improvement (beyond higher-resolution details) of the GCM results. Here, we investigate the bias patterns (offset during the historical period against observations) and climate change signals of two RCMs that have downscaled a comprehensive set of GCMs following the EURO-CORDEX framework. Our results show that the biases of the RCMs and GCMs are not additive and not independent. The two RCMs are systematically reducing the biases and modifying climate change signals of the driving GCMs, even on scales that are considered well resolved by the driving GCMs. The GCM projected summer warming at the end of the century is substantially reduced by both RCMs. These results are important, as the projected summer warming and its likely impact on the water cycle are among the most serious concerns regarding European climate change.

High-resolution ensemble projections and uncertainty assessment of regional climate change over China in CORDEX East Asia

Abstract. An ensemble simulation of five regional climate models (RCMs) from the coordinated regional downscaling experiment in East Asia is evaluated and used to project future regional climate change in China. The influences of model uncertainty and internal variability on projections are also identified. The RCMs simulate the historical (1980–2005) climate and future (2006–2049) climate projections under the Representative Concentration Pathway (RCP) RCP4.5 scenario. The simulations for five subregions in China, including northeastern China, northern China, southern China, northwestern China, and the Tibetan Plateau, are highlighted in this study. Results show that (1) RCMs can capture the climatology, annual cycle, and interannual variability of temperature and precipitation and that a multi-model ensemble (MME) outperforms that of an individual RCM. The added values for RCMs are confirmed by comparing the performance of RCMs and global climate models (GCMs) in reproducing annual and seasonal mean precipitation and temperature during the historical period. (2) For future (2030–2049) climate, the MME indicates consistent warming trends at around 1 ∘C in the entire domain and projects pronounced warming in northern and western China. The annual precipitation is likely to increase in most of the simulation region, except for the Tibetan Plateau. (3) Generally, the future projected change in annual and seasonal mean temperature by RCMs is nearly consistent with the results from the driving GCM. However, changes in annual and seasonal mean precipitation exhibit significant inter-RCM differences and possess a larger magnitude and variability than the driving GCM. Even opposite signals for projected changes in average precipitation between the MME and the driving GCM are shown over southern China, northeastern China, and the Tibetan Plateau. (4) The uncertainty in projected mean temperature mainly arises from the internal variability over northern and southern China and the model uncertainty over the other three subregions. For the projected mean precipitation, the dominant uncertainty source is the internal variability over most regions, except for the Tibetan Plateau, where the model uncertainty reaches up to 60 %. Moreover, the model uncertainty increases with prediction lead time across all subregions.