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This work presents a regional climate model, the Rossby Centre regional Atmospheric model (RCA1), recently developed from the High Resolution Limited Area Model (HIRLAM). The changes in the HIRLAM parametrizations, necessary for climate-length integrations, are described. A regional Baltic Sea ocean model and a modeling system for the Nordic inland lake systems have been coupled with RCA1. The coupled system has been used to downscale 10-year time slices from two different general circulation model (GCM) simulations to provide high-resolution regional interpretation of large-scale modeling. A selection of the results from the control runs, i.e. the present-day climate simulations, are presented: large-scale free atmospheric fields, the surface temperature and precipitation results and results for the on-line simulated regional ocean and lake surface climates. The regional model modifies the surface climate description compared to the GCM simulations, but it is also substantially affected by the biases in the GCM simulations. The regional model also improves the representation of the regional ocean and the inland lakes, compared to the GCM results. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to analyse the Finnish European Surveillance of Antimicrobial Consumption (ESAC) nursing home (NH) point prevalence surveys' (PPSs) data in detail, i.e. to evaluate the variability in the prevalence of antimicrobial prescription between NHs and its relationship to resident characteristics. Methods: All residents present in NHs for ≥24 h and receiving systemic antimicrobials on the day of the survey were included. Data on antimicrobials and their indications (prophylaxis or treatment, type of infection) were collected. Results: Three PPSs were performed: eight NHs participated in April and November 2009 and nine in May-September 2010. In total, there were 5,691 eligible residents (range by survey, 1,706-2,320; range by NH, 60-688), 716 (12.6 %; range by NH, 3.2-33.3 %) of which received at least one antimicrobial and 40 residents received two. The most common indication was prophylaxis (487/5,691, 8.6 %), mainly for urinary tract infection (UTI) (460/487, 94.5 %). Of the residents, 269/5,691 (4.7 %, range by NH, 1.5-6.0 %) were on antimicrobial treatment. UTI (119/269; 44.2 %) was the most common indication for treatment. Methenamine (306/756, 40.5 %) was the most commonly used antimicrobial, followed by trimethoprim (13.6 %) and pivmecillinam (11.0 %). In the eight NHs participating in all three surveys, the prevalence of residents receiving antimicrobials decreased from 16.6 to 9.7 %. Conclusions: Antimicrobial use was common in NHs in Finland and most were used for UTI prophylaxis and treatment. The usage, however, varied among NHs and tended to decrease during the surveys. NHs may benefit from antimicrobial stewardship interventions focused on UTI. [ABSTRACT FROM AUTHOR]

Urbanization has made significant changes in Land Use Land Cover (LULC) patterns and these changes have a considerable impact on Land Surface Temperature (LST) in highly populated and rapidly growing cities. The current study aims to analyze the past and future LULC changes and seasonal (summer and winter) LST patterns in Herat City, Afghanistan. Random Forest (RF) algorithm and Landsat thermal bands were applied to retrieve LULC changes and associated LST patterns during 2000 and 2020. In addition, the Cellular Automata-Markov (CA-Markov) and the CA-Logistic Regression (CA-LR) models were used to predict the future LULC and seasonal (summer and winter) LST patterns, respectively for the year 2030. The CA-Markov and CA-LR models’ validation for LULC and LST were assured using the predicted and actual data for the year 2020. Results showed a significant increase in the built-up area about 48.06 km2 (42.53%), while the vegetation 27.65 km2 (24.48%) and bare soil 20.45 km2 (18.11%) reduced significantly, however, the water bodies decreased slightly in Herat City. The seasonal (summer and winter) LST patterns revealed higher temperatures in built-up area followed by the bare soil, vegetation and water bodies. Predicted LULC patterns showed a considerable increase in the built-up area of about 16.69 km2 (14.77%) for the year 2030 as compared to 2020. Seasonal analysis predicts that by 2030, around 51.84 km2 (45.88%) of Herat City may experience summer LST > 45 °C, while 90.15 km2 (79.78%) could have winter LST ranging from 10–20 °C. The results indicated a direct relation between rapid urbanization and rising LST, which negatively impacts both the local population and the environment. To ensure sustainable urban growth and reduce the impact of LST in Herat City, the future city master plans should prioritize urban greenery and the protection of natural resources. [ABSTRACT FROM AUTHOR]

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of −9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (−0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows. [ABSTRACT FROM AUTHOR]

The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on "Convective phenomena at high resolution over Europe and the Mediterranean", a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas. [ABSTRACT FROM AUTHOR]

Climate models are fundamental tools to estimates the reliable future climate change and its effects on the water resources and agriculture in basins. However, all climate models are not equally performed for all areas. Therefore, determining the most appropriate climate models for a specific study area is essential. The focus of this study was to evaluate the performance of the regional climate models with regard to simulating precipitation, and temperatures at Katar watershed. This study examines the performance of fourteen CORDEX-AFRICA-220 Regional Climate Models (RCMs) for the period of 1984–2005 using statistical metrics such as Pearson correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and bias. The findings indicated that GERICS-MPI was better performed in representing Areta, and Bokoji station, GERICS-IPSL was better representing in Assela, Ketergenet, and Sagure station, CCCma-CanESM2-AFR22, and RCA4-ICHEC performed relatively better in representing the mean annual observed rainfall at the Kulumsa, and Ogolcho station respectively. However, RCA4-CSIRO performed weakly in estimation of annual rainfall at all stations. RCM model such as GERICS-MPI was relatively better than the others in replicating the annual pattern of the maximum temperature at Areta, Bokoji, and Ketergenet stations. Similarly, GERICS-IPSL were relatively better in replicating the annual maximum temperature at Assela, and Sagure stations, CCCma-CanESM2-AFR22 at Kulumsa station, and RCA4-ICHEC at Ogolcho station performed well in capturing the observed and simulated annual maximum temperature. Better performance was observed on minimum temperature at CCCma-CanESM2-AFR22 at Areta, Assela, and Ketergenet stations, GERICS-MOHE-AFR-22 at Bokoji station, GERICS-MPI at Kulumsa, and Ogolcho stations, RAC4-NOAA-2G at Sagure stations. However, weak performance was observed RCA4-CSIRO at all stations. RCM models of GERICS-MPI, and CCLM4-NCC-AFR-22 performed better than the other RCM models for correction of annual rainfall in Katar watershed. However, poor performance was observed at RCA4-ICHEC model on Katar watershed. The GERICS-MPI model performed well. However, poor performance was observed at RCA4-ICHEC on maximum temperature, and GERICS-NOAA-2M on minimum temperature in Katar watershed. [ABSTRACT FROM AUTHOR]

Engineering biology (EngBio) is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. EngBio applications could make a significant contribution to achieving net zero greenhouse gas emissions. Yet, policy support will be needed if EngBio is to fulfil its climate mitigation potential. What form should such policies take, and what EngBio applications should they target? This paper reviews EngBio's potential climate contributions to assist policymakers shape regulations and target resources and, in so doing, to facilitate democratic deliberation on desirable futures. Engineering biology is a dynamic field that uses gene editing, synthesis, assembly, and engineering to design new or modified biological systems. Here the authors discuss the policy considerations and interventions needed to support a role for engineering biology in climate change mitigation. [ABSTRACT FROM AUTHOR]

The current study aimed to assess the ability of Global Climate Models (GCMs) from the third, fifth, and sixth phases of the Coupled Model Intercomparison Project (CMIP) to reproduce observed rainfall over the Western Himalaya Region (WHR) between 1901 and 2000. A probabilistic approach was adopted to evaluate the models' skill in capturing the observed precipitation from 22 India Meteorological Department stations by comparing their probability distribution function (PDF) with that of the observed rainfall. The results indicated that all three generations of climate models could adequately reproduce the PDF of observed precipitation, surpassing the observed PDFs in both annual and monsoon seasons. Subsequently, each model was assigned a rank based on its skill score. The Geophysical Fluid Dynamics Laboratory model from CMIP3, the community Earth system model from CMIP5, and the CMIP6 model from the Met Office Hadley Centre achieved the highest ranks, accurately capturing the distribution of observed precipitation over WHR. Remarkably, the Japanese model from the Model for Interdisciplinary Research and the UK model from Hadley Centre exhibited consistently high performance across all three CMIP generations. Overall, the top-ranked models from each generation can be deemed reliable for use by researchers and decision-makers concerned with the impact of climate change on the Western Himalaya Region. [ABSTRACT FROM AUTHOR]

The intensification of heatwaves dues to climate change is a significant concern, with substantial impacts on ecosystems and human health, particularly in developing countries. This study utilizes NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and projected population data accounting for China's population policies to project changes in various grades of heatwaves (light, moderate, and severe) and the population exposure to heatwaves (PEH) in Xinjiang under three shared socioeconomic pathways (SSP1–2.6, SSP2-4.5, and SSP5-8.5). The results show that the number of days and intensity of heatwaves in Xinjiang are projected to increase. Heatwaves occurring in Xinjiang will predominantly be severe heatwaves (SHW) in the long-term under the SSP5-8.5 scenario, and the number of SHW days projected to increase by 62 ± 18.4 days compared to the reference period. Changes in heatwaves are anticipated to influence PEH, estimating population exposure to light, moderate, and severe heatwaves (LPEH, MPEH, and SPEH) at 534.6 ± 64 million, 496.2 ± 43.5 million, and 1602.4 ± 562.5 million person-days, respectively, in the long-term under the SSP5-8.5 scenario. The spatial distribution of PEH is projected to be consistent with that of the reference period, with high values persisting in Urumqi, Kashgar and Hotan. Changes in PEH are primarily driven by climate effects, followed by interactive effects, while population effects contribute the least. Therefore, mitigating climate change is crucial to reduce the PEH in Xinjiang. [ABSTRACT FROM AUTHOR]

This study assesses the ability of climate models to represent rainy season (RS) dependent climate indices relevant for agriculture and crop-specific agricultural indices in eleven African subregions. For this, we analyze model ensembles build from Regional Climate Models (RCMs) from CORDEX-CORE (RCM_hist) and their respective driving General Circulation Models (GCMs) from CMIP5 (GCM_hist). Those are compared with gridded reference data including reanalyses at high spatio-temporal resolution (≤ 0.25°, daily) over the climatological period 1981–2010. Furthermore, the ensemble of RCM-evaluation runs forced by ERA-Interim (RCM_eval) is considered. Beside precipitation indices like the precipitation sum or number of rainy days annually and during the RS, we examine three agricultural indices (crop water need (CWN), irrigation requirement, water availability), depending on the RS' onset. The agricultural-relevant indices as simulated by climate models, including CORDEX-CORE, are assessed for the first time over several African subregions. All model ensembles simulate the general precipitation characteristics well. However, their performance strongly depends on the subregion. We show that the models can represent the RS in subregions with one RS adequately yet struggle in reproducing characteristics of two RSs. Precipitation indices based on the RS also show variable errors among the models and subregions. The representation of CWN is affected by the model family (GCM, RCM) and the forcing data (GCM, ERA-Interim). Nevertheless, the too coarse resolution of the GCMs hinders the representation of such specific indices as they are not able to consider land surface features and related processes of smaller scale. Additionally, the daily scale and the usage of complex variables (e.g., surface latent heat flux for CWN) and related preconditions (e.g., RS-onset and its spatial representation) add uncertainty to the index calculation. Mostly, the RCMs show a higher skill in representing the indices and add value to their forcing models. [ABSTRACT FROM AUTHOR]

We investigate the influence of bias correction of Global Climate Models (GCMs) prior to dynamical downscaling using regional climate models (RCMs), on the change in climate projected. We use 4 GCMs which are bias corrected against ERA-Interim re-analysis as a surrogate truth, and carry out bias corrected and non-bias corrected simulations over the CORDEX Australasia domain using the Weather Research and Forecasting model. Our results show that when considering the effect of bias correction on current and future climate separately, bias correction has a large influence on precipitation and temperature, especially for models which are known to have large biases. However, when considering the change in climate, i.e the Δ change (future minus current), we found that while differences between bias-corrected and non-corrected RCM simulations can be substantial (e.g. more than 1 ∘ C for temperatures) these differences are generally smaller than the models' inter-annual variability. Overall, averaged across all variables, bias corrected boundary conditions produce an overall reduction in the range, standard deviation and mean absolute deviation of the change in climate projected by the 4 models tested, over 61.5%, 62% and 58% of land area, with a larger reduction for precipitation as compared to temperature indices. In addition, we show that changes in the Δ change for DJF tasmax are broadly linked to precipitation changes and consequently soil moisture and surface sensible heat flux and changes in the Δ changefor JJA tasmin are linked to downward longwave heat flux. This study shows that bias correction of GCMs against re-analysis prior to dynamical downscaling can increase our confidence in projected future changes produced by downscaled ensembles. [ABSTRACT FROM AUTHOR]

In this study, the existing empirical models for sediment management for ungauged watersheds in South Korea developed in 2019 were updated. The existing models were developed based on the Universal Soil Loss Equation structure with domestic river sediment data obtained from 2005 to 2014 and watershed characteristic values. To update the models, the specific degradation (SD) value was calculated first in the same way as the existing method by adding sediment measurement and daily discharge data obtained from 2015 to 2020. The calculated SD values with recent data decreased compared with the past and differed from the results predicted using the existing models and the watershed characteristics investigated in this study. For this reason, the existing models were updated using the latest watershed characteristics and SD values. The root-mean-square error values of the existing models, which ranged from 131 to 90 tons/km2·year, decreased to 102 to 62 tons/km2·year in the updated models with the same structures. Multicollinearity was tested to evaluate the reliability of the updated models, and a new 95% prediction interval for usability was estimated. In addition, quantitative changes in the future sediment load of domestic rivers were analyzed using the future rainfall data obtained from climatic change scenarios and the watershed area. The results of this study reconfirmed that periodic updates are required for the existing empirical models. It is expected that the empirical models will be used in various ways, such as for the prediction of future sediment loads for river sediment management. [ABSTRACT FROM AUTHOR]

The Himalayan region is one of the world's youngest and greatest mountain ranges, serving as the source of several major rivers in Asia. Nepal, situated in the middle of the Himalayas, has a diverse climate that varies from tropical to polar, with highly heterogeneous precipitation patterns and hydrological regimes. This study analyzes long-term changes in water balance parameters from 1961 to 2020 across Nepal using TerraClimate (a global gridded dataset), splitting the period into two 30-year phases, reflecting pre-development (1961–1990) and development (1991–2020). The water balance parameters include precipitation, actual and potential evapotranspiration, runoff, climate water deficit, soil moisture, minimum temperature, and maximum temperature. These parameters illustrate the various in- and out-fluxes of a hydrologic system and the net change in water storage. This study also examines the temporal variation and monotonic trends in water balance in different physiographical and spatial regions. We divide the country into nine regions, and additionally, look at seasonal and annual scales. Results show that maximum and minimum temperatures have an increasing trend and, therefore, indicate a positive deviation during the later period (1991–2020) compared to the earlier period (1961–1990) in all regions. Furthermore, we find that precipitation and soil moisture are slightly decreasing in the later period, while climate water deficit is increasing, indicating increased water stress, particularly in the western mountains. In addition, several springs in the mountains of Nepal are disappearing, which indicates an increase in water scarcity and vulnerability. Seasonally, post-monsoon and winter temperatures are increasing faster than pre-monsoon and monsoon temperatures. Looking at the precipitation pattern, monsoon and post-monsoon precipitation are decreasing overall, and pre-monsoon precipitation is mostly increasing. These findings can help to better comprehend water availability in a region and guide water management strategies. [ABSTRACT FROM AUTHOR]

This study examined the long-term (1980–2019) spatio-temporal trends, variability and teleconnections of Indian summer monsoon rainfall (ISMR) of all districts of Haryana, India and their impact on agricultural productivity. The gridded datasets of India Meteorological Department (IMD) were used to statistically analyse the rainfall distribution, trend, coefficient of variation and intensity of rainfall. The gridded datasets of European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were examined for lower and upper tropospheric wind circulation (850 hPa and 200hpa), vertically integrated moisture transport (VIMT) and surface moisture flux (SMF). The datasets of National Oceanic and Atmospheric Administration (NOAA) were correlated with ISMR and composite deviation of rainfall and rainfall intensity during El Niño and La Niña from neutral years was examined at district level. Our analysis revealed that districts lying in eastern agroclimatic zone (EAZ) of Haryana received more ISMR during each month of monsoon season as compared to the ones situated in western agroclimatic zone (WAZ). Trend-free pre-whitening Mann–Kendall (TFPW-MK) test revealed that Kurukshetra, Panipat, Ambala, Rohtak, Faridabad, Jhajjar, Sonipat, Fatehabad and Palwal have shown a decreasing trend while Mahendragarh and Panchkula have shown an increasing trend of rainfall. During the El Niño years, most of the locations in the state received deficient to large deficient category, whereas during the La Niña episodes, most of the locations received excess to large excess category of ISMR, which is indicative of the influence of El Niño–Southern Oscillation (ENSO) on the regional scale. The influence of ISMR on bajra productivity for the districts lying in WAZ and rice productivity for the districts lying in EAZ was undertaken. This study is beneficial for understanding the impacts of climate change and climate variability on ISMR dynamics in Haryana which may further guide the policy-makers and beneficiaries for optimising the use of hydrological resources. [ABSTRACT FROM AUTHOR]

Evidence is mounting that the 1.5 °C goal of the Paris Agreement is unrealistic. But, contrary to many in the scientific community, major NGOs have remained steadfast in their support for this target. We argue that this is because "unrealistic" or aspirational targets can serve important political objectives, including becoming focal points for coalition-building and broad-based mobilization and allowing for effective naming and shaming. [ABSTRACT FROM AUTHOR]

Climate indices are often used as a climate monitoring tool, allowing us to understand how the frequency, intensity, and duration of extreme weather events are changing over time. Here, based on complex statistical analysis we identify highly correlated significant pairs of compound events at the highest spatial resolution, on a monthly temporal scale across Europe. Continental-scale monthly analysis unleashes information on compound events such as high-risk zones, hotspots, monthly shifts of hotspots and trends, risk exposure to land cover and population, and identification of maximum increasing trends. While there are many studies on single or compound climate extremes there are only a few studies that addresses the relationship between pairs of hazards, the incorporation of bioclimatic indices, the determination of a grid best-fit copula approach, and the outlining relevance of this work of compound event risks with exposures. In this respect, here, using 27-bivariate and 10-trivariate copula models, we show that the different hazard pairs have high combined risks of indices related to radiation, temperature, evapotranspiration, bioclimatic-based indices, such as the universal thermal climate index, wind chill index, and heat index, mainly over the northern and eastern European countries. Furthermore, we show that over the last 7 decades, agricultural and coastal areas are highly exposed to the risks of defined hotspots of compound events. In some of the hotspots of compound events-identified by clusters, there is no monthly shifts of hotspots, leading to higher impacts when compounded. Future work needs to integrate the framework and process to identify other compound pairs. [ABSTRACT FROM AUTHOR]

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen. [ABSTRACT FROM AUTHOR]

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events. [ABSTRACT FROM AUTHOR]

The influence of climate parameters on evaporation during the period of active vegetation of plants in the forest-steppe, steppe, and semi-desert in the south of European Russia in the period 1980–2021 was studied. The linkage between evaporation and soil moisture have been considered. At the beginning of the growing season, the change in evaporation in the forest and steppe is largely determined by a positive linkage with temperature and wind stress. In the middle of the growing season, the role of precipitation and wind increases. From July, the linkage between evaporation and temperature becomes negative due to the increasing moisture deficit in the regions that are characterized by dry sub-humid and semi-arid climates. The linkage between evaporation and moisture content of the upper soil layer is negative at the beginning of the growing season with minimal evaporation from the still unheated soil cover in the forest-steppe and steppe, and is positive in the semi-desert due to already heated soil. From the middle to the end of the growing season the linkage between evaporation and soil moisture is positive from forest-steppe to semi-desert landscapes. At the beginning of the growing season, continued warming leads to an increase in evaporation; in the middle and at the end of the growing season, evaporation trends are significantly negative, since the lack of moisture at high temperatures leads to the drying of the soil cover and premature withering of vegetation. The greatest decrease in evaporation is observed in the steppe and dry steppe, which was affected by the drying up of the area with an increase in temperature, a decrease in the amount of precipitation and the average wind speed. Topsoil moisture trends are mostly negative throughout the south of European Russia, with the maximum moisture loss in the broad-leaved-forest and forest-steppe, which are more humid than dry-steppe and semi-desert. In the semi-desert, changes in soil moisture are minimal over the entire growing season. [ABSTRACT FROM AUTHOR]

We present the Adriatic atmosphere–ocean trends and variability simulated by the kilometer-scale Adriatic Sea and Coast (AdriSC) climate model during the 1987–2017 period. As the AdriSC model has been successfully validated over the entire basin against an extensive dataset of in situ measurements and remote sensing products, the reliability of the presented results at the regional (basin-wide) and local (sub-domains) scales is high. We found that trends and variability in the atmosphere reveal strong land-sea contrasts with (1) stronger temperature trends associated with lower, mostly seasonal, variability over the Adriatic Sea than over the land and (2) positive trends of wind speed and negative trends of relative humidity associated with high, mostly seasonal, variability over the sea and vice versa over the land. While, in the ocean, the analysis highlights several processes: (1) extensive warming by the atmosphere at the surface during summer, affecting both temperature and salinity, (2) shallowing of the advection of the saline Levantine Intermediate Water inflow into the Adriatic, (3) decrease of the Adriatic deep water outflow and therefore the Adriatic-Ionian thermohaline circulation, (4) warming of near-bottom waters, in particular in the middle and northern Adriatic, and (5) shrinking and weakening of the Southern Adriatic Gyre, in particular at its center. We thus demonstrate that kilometer-scale coupled atmosphere–ocean modelling is an indispensable tool for proper quantification of climate change in complex coastal basins, as it captures local characteristics not properly reproduced by present state-of-the-art regional climate models with an order of magnitude coarser resolutions. [ABSTRACT FROM AUTHOR]

Estimating the impact of climate change and emission scenarios on air pollution can be done using regional climate models (RCMs). Climate uncertainties are commonly estimated using RCM ensembles such as provided by EURO-CORDEX. Despite the strong relations between the weather and air pollutants, interactions are usually complex and require meteorological parameters that are not commonly available for the RCM ensembles. Pollution peaks, however, often coincide with stagnant atmospheric conditions that can be captured with widely-available RCM data. We first show that a commonly-used atmospheric stability index that uses rainfall, near-surface and 500 hPa wind speed, relates well to average and extreme air pollutant concentrations over Europe using Copernicus Atmosphere Monitoring Service (CAMS) data. We then provide an in-depth validation of 25 RCMs to reproduce the spatio-temporal features of air stagnation by comparison with ERA5. Overall the models were found to reproduce stagnant episodes fairly well, especially after bias correction. The systematic underestimation of stagnation frequency and duration is traced back to overestimated near-surface wind speed for a large group of models at high-elevation regions where the temporal correlations are also low. Regardless of the reference dataset, two model groups are identified that, independent on their resolution, give strongly different results in terms of orographic dependence of surface wind speed. These strong discrepancies underscore the need for bias correction when using RCM data for analysis of stagnation episodes. [ABSTRACT FROM AUTHOR]

High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs. [ABSTRACT FROM AUTHOR]

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)–Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of − 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, − 7.08 to 20.04%, − 7.35 to 57%, and − 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [ABSTRACT FROM AUTHOR]

We here inter-compare four different tracking algorithms by applying them onto the precipitation fields of an ensemble of convection-permitting regional climate models (cpRCMs) and on high-resolution observational datasets of precipitation. The domain covers the Alps and the northern Mediterranean and thus we here analyse heavy precipitation events, that are renowned for causing hydrological hazards. In this way, this study is both, an inter-comparison of tracking algorithms as well as an evaluation study of cpRCMs in the Lagrangian frame of reference. The tracker inter-comparison is performed by comparison of two case studies as well as of climatologies of cpRCMs and observations. We find that that all of the trackers produce qualitatively equal results concerning characteristic track properties. This means that, despite of quantitative differences, equivalent scientific conclusions would be drawn. This result suggests that all trackers investigated are reliable analysis tools of atmospheric research. With respect to the model ensemble evaluation, we find an encouraging performance of cpRCMs in comparison to radar-based observations. In particular prominent hotspots of heavy precipitation events are well-reproduced by the models. In general most characteristic properties of precipitation events have positive biases. Assuming the under-catchment of precipitation in observations in a domain of such complex orography, this result is to be expected. Only the mean area of tracks is underestimated, while their duration is overestimated. Mean precipitation rate is estimated well, while maximum precipitation rate is overestimated. Furthermore, geometrical and rain volume are overestimated. We find that models overestimate the occurrence of precipitation events over all mountain chains, whereas over plain terrain in summer precipitation events are seen underestimated. This suggests that, despite the convection-permitting resolution, thermally driven thunderstorms are either not triggered or their dynamics still under-resolved. Eventually we find that biases in the spatio-temporal properties of precipitation events appear reduced when evaluating cpRCMs against Doppler radar-based and rain gauge-adjusted observational datasets of comparable spatial resolution, strengthening their role in evaluation studies. [ABSTRACT FROM AUTHOR]

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘ C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region. [ABSTRACT FROM AUTHOR]

The Arafura and Timor Seas region is shared by Indonesia, Timor Leste, Australia, and Papua New Guinea (PNG), and is at the intersection of the Pacific and Indian oceans. High coastal population densities, degraded habitats, overexploited fisheries, low profile coasts, shallow continental shelves and macro-tidal conditions mean that coastal and marine environments in the region are currently facing multiple pressures. Climate change is expected to exacerbate these pressures and have profound effects on the status and distribution of coastal and marine habitats, the fish and invertebrates they support and, therefore, dependent communities and industries. Downscaled climate change projections for 2041–2070 for air and sea temperature, ocean chemistry and rainfall were modelled to provide spatially relevant regional data for a structured semi-quantitative vulnerability assessment. Results of the assessment were spatially variable and identified shallow coral reefs as highly vulnerable, particularly in the Timor-Leste and Indonesia-Arafura sub-regions. Seagrass meadows were most vulnerable in the Gulf of Carpentaria, Indonesia-Arafura, and Timor-Leste sub-regions. Mangrove habitats were most vulnerable in Timor-Leste and Western PNG sub-regions. Drivers of vulnerability include poor habitat condition, non-climate pressures, low connectivity, and limited formal management. Marine species vulnerability was also spatially variable, with highly vulnerable and priority species identified for each sub-region, including finfish and marine invertebrates. A key driver of species vulnerability was their stock status, with many species in Timor-Leste, Western PNG and Indonesia, and several in northern Australia, overfished or potentially overfished. Limited management in some sub-regions, as well as non-climate pressures such as habitat decline, poor water quality and illegal, unregulated and unreported fishing were also key drivers. Species of conservation interest (dugong and marine turtles) were also highly vulnerable to climate change, driven by their threatened status and the fact that they are low productivity species that take years to recover from impacts. Priority species and habitats for local action were identified and current pressures that undermine condition and/or resilience, with strategic recommendations aimed at minimising climate change vulnerability. [ABSTRACT FROM AUTHOR]

The Asian monsoon provides the freshwater that a large population in Asia depends on, but how anthropogenic climate warming may alter this key water source remains unclear. This is partly due to the prevailing point-wise assessment of climate projections, even though climate change patterns are inherently organized by dynamics intrinsic to the climate system. Here, we assess the future changes in the East Asian summer monsoon precipitation by projecting the precipitation from several large ensemble simulations and CMIP6 simulations onto the two leading dynamical modes of internal variability. The result shows a remarkable agreement among the ensembles on the increasing trends and the increasing daily variability in both dynamical modes, with the projection pattern emerging as early as the late 2030 s. The increase of the daily variability of the modes heralds more monsoon-related hydrological extremes over some identifiable East Asian regions in the coming decades. Projecting regional hydrological response to climate change has been a longstanding challenge. By using the dominant precipitation modes as "fingerprints", this study shows the robust Asian monsoon rainfall response to anthropogenic warming. [ABSTRACT FROM AUTHOR]

Northwest China (NWC) is experiencing noticeable climate change accompanied with increasing impacts of climate hazards induced by changes in climate extremes. Towards developing climate adaptation strategies to mitigate the negative climatic impacts on both the ecosystem and socioeconomic system of the region, this study investigates systematically the spatial patterns of climate change and the associated climate hazards across NWC based on high resolution reanalysis climate dataset for the period 1979 to 2018. We find that NWC overall is under a warming and wetting transition in climate with change rate of temperature and precipitation around 0.49 °C/10a and 22.8 mm/10a respectively. Characteristics of climate change over the NWC however vary considerably in space. According to significance of long-term trends in both temperature and aridity index for each 0.1° × 0.1° grids, five types of climate change are identified across NWC, including warm-wetting, warm-drying, warm without wetting, wetting without warming and unchanging. The warm-wetting zone accounts for the largest proportion of the region (41%) and mainly locates in the arid or semi-arid northwestern NWC. Our findings show most region of NWC is under impacts of intensifying heatwave and rainstorm due to significant increases in high temperature extremes and precipitation extremes. The warming but without wetting zone is found under a more severe impact of heatwave, particularly for areas near northern Mount. Qinling and northern Loess Plateau. Areas with stronger wetting trend is suffering more from rainstorm. [ABSTRACT FROM AUTHOR]

Landscape pattern and climate change impact ecosystem services and consequently the sustainability of regions and the entire earth system. Yet, we lack an adequate understanding of the interactions among landscape pattern, climate change, and ecosystem services, or the landscape-climate-ecosystems (LCE) nexus. Improving our understanding of the LCE nexus can help create multifunctional landscapes that are resilient to environmental and socioeconomic perturbations. Thus, the main objectives of this study were as follows: (1) to review and synthesize the existing studies of the LCE nexus and (2) to identify research gaps and discuss future research directions to better understand the LCE nexus. Using the SCOPUS database, we followed the procedure of a systematic review to search and review articles that simultaneously addressed multiple links in the LCE nexus. Our review revealed that landscape governance and planning play an important role in determining the composition and configuration of regional landscapes. In addition, changes in landscape pattern and climate can variably impact provisioning, regulating, and cultural ecosystem services. Although many studies have addressed individual links in the LCE nexus, multiple-step linkages have been rarely addressed. Moving forward, therefore, we should focus more on multi-step linkages and the entire LCE nexus to better understand the feedback loops within the nexus. Doing so is necessary for improving landscape and regional sustainability. In addition, future research needs transdisciplinary approaches that integrate the social and ecological systems in regional landscapes to understand not only how landscape pattern and climate change influence ecosystem services, but also how changes in ecosystem services feedback to affect landscape pattern. [ABSTRACT FROM AUTHOR]

As a result of warming and precipitation deficits, the increasing shortage of water resources, droughts have become one of the main drivers of desertification, land degradation and food insecurity with direct impacts on ecosystems and society, especially in fragile communities. Over the Iberian Peninsula, a known climate change hotspot, the occurrence of droughts varies in intensity and severity, being its assessment under present and future conditions an important tool for adaptation measures. Here, for the first time, we present a comprehensive analysis of different plausible evolutions of droughts throughout the twenty-first century over Iberia on a monthly basis, featuring three different emission scenarios (RCP2.6, RCP4.5, RCP8.5). A multi-variable, multi-model EURO-CORDEX weighted ensemble is used to assess future drought conditions using the SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index). All indexes were computed by considering the full period, from 1971 to 2000 merged with 2011–2100 from each RCP scenario. The results clearly show that the Iberian Peninsula is highly vulnerable to climate change, indicating a significant increase in the intensity and severity of drought occurrences, even for the low-end RCP2.6 scenario. For the RCP4.5 and RCP8.5 scenarios, the increases are more pronounced and enhanced throughout the twenty-first century, from 3 up to 12 more severe droughts for the shorter timescales with increases in mean duration above 30 months for the longer accumulation periods. The use of all the RCPs data pooled together with a multi-variable weighted ensemble approach allows not only a more accurate and robust projection of future droughts but also ensures comparability among the projections from the three RCP scenarios. The future drought evolution aspires to assist the new Portuguese national roadmap for adaptation for the twenty-first century, bridging the water sector challenges from mitigation to adaptation in a dynamic way. [ABSTRACT FROM AUTHOR]

The study projects climate over the Upper Indus Basin (UIB), covering geographic areas in India, Pakistan, Afghanistan, and China, under the two Representative Concentration Pathways (RCPs), viz., RCP4.5 and RCP8.5 by the late twenty-first century using the best-fit climate model validated against the climate observations from eight meteorological stations. GFDL CM3 performed better than the other five evaluated climate models in simulating the climate of the UIB. The model bias was significantly reduced by the Aerts and Droogers statistical downscaling method, and the projections overall revealed a significant increase in temperature and a slight increase in precipitation across the UIB comprising of Jhelum, Chenab, and Indus sub-basins. According to RCP4.5 and RCP8.5, the temperature and precipitation in the Jhelum are projected to increase by 3 °C and 5.2 °C and 0.8% and 3.4% respectively by the late twenty-first century. The temperature and precipitation in the Chenab are projected to increase by 3.5 °C and 4.8 °C and 8% and 8.2% respectively by the late twenty-first century under the two scenarios. The temperature and precipitation in the Indus are projected to increase by 4.8 °C and 6.5 °C and 2.6% and 8.7% respectively by the late twenty-first century under RCP4.5 and RCP8.5 scenarios. The late twenty-first century projected climate would have significant impacts on various ecosystem services and products, irrigation and socio-hydrological regimes, and various dependent livelihoods. It is therefore hoped that the high-resolution climate projections would be useful for impact assessment studies to inform policymaking for climate action in the UIB. [ABSTRACT FROM AUTHOR]

Climate change is one of the primary causes of species redistribution and biodiversity loss, especially for threatened and endemic important plant species. Therefore, it is vital to comprehend "how" and "where" priority medicinal and aromatic plants (MAPs) might be effectively used to address conservation-related issues under rapid climate change. In the present study, an ensemble modelling approach was used to investigate the present and future distribution patterns of Aquilegia fragrans Benth. under climate change in the entire spectrum of Himalayan biodiversity hotspot. The results of the current study revealed that, under current climatic conditions, the northwest states of India (Jammu and Kashmir, Himachal Pradesh and the northern part of Uttarakhand), the eastern and southern parts of Pakistan Himalaya have highly suitable climatic conditions for the growth of A. fragrans. The ensemble model exhibited high forecast accuracy, with temperature seasonality and precipitation seasonality as the main climatic variables responsible for the distribution of the A. fragrans in the biodiversity hotspot. Furthermore, the study predicted that future climate change scenarios will diminish habitat suitability for the species by −46.9% under RCP4.5 2050 and −55.0% under RCP4.5 2070. Likewise, under RCP8.5, the habitat suitability will decrease by −51.7% in 2050 and −94.3% in 2070. The current study also revealed that the western Himalayan area will show the most habitat loss. Some currently unsuitable regions, such as the northern Himalayan regions of Pakistan, will become more suitable under climate change scenarios. Hopefully, the current approach may provide a robust technique and showcases a model with learnings for predicting cultivation hotspots and developing scientifically sound conservation plans for this endangered medicinal plant in the Himalayan biodiversity hotspot. [ABSTRACT FROM AUTHOR]

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3–0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10 mm and 8.9 mm (6%, 39.1 mm, 39.1 mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50 mm/15 mm in the near future (2011–2040) for lower and 20 mm/6 mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2. [ABSTRACT FROM AUTHOR]

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: (1) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and (2) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability. Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value. [ABSTRACT FROM AUTHOR]

The ability of a suite of models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate observed large-scale atmospheric circulation patterns over the Pacific Northwest of North America is evaluated. Twelve representative patterns of 500-hPa geopotential height (Z500) anomalies are identified using the self-organizing map method applied to reanalysis. CMIP6 Z500 anomalies from simulations of the historical period are mapped to each reanalysis-derived pattern, and the resulting differences between patterns, as well as differences in frequency of pattern occurrence, are quantified. In general, models are able to simulate the range of Z500 patterns with reasonable fidelity, although model skill varies across the 25-member ensemble. Surface temperature and precipitation anomalies associated with each Z500 pattern are found to also be reasonably simulated by the models, with some biases noted. This boosts confidence that the models are simulating temperature and precipitation patterns for the correct physical reasons. The models exhibit a range of skill at simulating pattern occurrence frequency and pattern persistence, with more agreement in winter than summer. Results indicate that the CMIP6 models are appropriate for assessing future projections of key atmospheric circulation patterns and their impacts on temperature and precipitation over the region. [ABSTRACT FROM AUTHOR]

Cities have a significant impact on the environment, forming microclimatic features such as urban heat island, an increase in the intensity of convective weather events, etc. Numerical models of the atmosphere with an integrated block that describes the interaction between the urbanized surface and the atmosphere—urban parameterization—are good at reproducing the meteorological features of the urban environment. Reviews of urban parameterizations are mostly outdated, and recent ones do not fully cover aspects of the methods used in the models to describe physical processes. This paper is dedicated to updating information on urban parameterizations, comparing the approaches used in them to describe physical processes, and forming proposals for their improvement. Based on the most common urban parameterizations of various levels of complexity, the main groups of physical processes describing "urban surface-atmosphere" interaction are identified. They are the surface energy balance, radiation heat transfer, surface moisture balance, turbulent heat and moisture exchange in the urban canopy, anthropogenic influence on heat and moisture fluxes, radiation, and turbulent interaction with urban vegetation. The main approaches to the parameterization of physical processes are defined within each block. Modern trends in the development of urban parameterizations are highlighted: (1) over the past 10 years, parameterizations have become more complicated due to the addition of the building energy model, a three-dimensional structure of urban vegetation, and vertical resolution when calculating turbulent fluxes; (2) at the same time, not much attention is paid to revising the original empirical formulas, often obtained on the basis of single field or laboratory experiments. Ways to improve urban parameterizations are proposed by clarifying the basic dependencies used mainly in the calculation of turbulent fluxes, particularly using the results of highly detailed large-eddy simulation modeling, which, with growing computational power, is increasingly used to simulate explicit heat transfer between the atmosphere and individual elements of the urban environment. [ABSTRACT FROM AUTHOR]

Vector-borne parasites may be transmitted by multiple vector species, resulting in an increased risk of transmission, potentially at larger spatial scales compared to any single vector species. Additionally, the different abilities of patchily distributed vector species to acquire and transmit parasites will lead to varying degrees of transmission risk. Investigation of how vector community composition and parasite transmission change over space due to variation in environmental conditions may help to explain current patterns in diseases but also informs our understanding of how patterns will change under climate and land-use change. We developed a novel statistical approach using a multi-year, spatially extensive case study involving a vector-borne virus affecting white-tailed deer transmitted by Culicoides midges. We characterized the structure of vector communities, established the ecological gradient controlling change in structure, and related the ecology and structure to the amount of disease reporting observed in host populations. We found that vector species largely occur and replace each other as groups, rather than individual species. Moreover, community structure is primarily controlled by temperature ranges, with certain communities being consistently associated with high levels of disease reporting. These communities are essentially composed of species previously undocumented as potential vectors, whereas communities containing putative vector species were largely associated with low levels, or even absence, of disease reporting. We contend that the application of metacommunity ecology to vector-borne infectious disease ecology can greatly aid the identification of transmission hotspots and an understanding of the ecological drivers of parasite transmission risk both now and in the future. [ABSTRACT FROM AUTHOR]

Quantitative and qualitative knowledge about the potential impacts of climate change on extreme hydrological events is crucial for water resource management and extreme risk management under climate change. This has theoretical and realistic implications to study and couple the climate system with hydrologic processes, to understand the system and solve related problems in water resources and extreme hydrology, such as decision making, plan management, environmental protection, and ecological balance. This paper reviews recent studies investigating climate change impact on hydrological extremes using a perspective of the integrated modeling framework comprising climate change scenarios, climate models, bias correction methods, hydrological modeling (model structure and parameterization), and reducible uncertainty arising from these sources characterized by a paucity of knowledge. The available research outcomes show the extreme high flows are likely to increase under climate change in the most parts of Europe, Asia, and the USA, but greatly vary and decrease in Africa and Latin America, which is highly variable and uncertain in space and time. Each component in integrated modeling has an important role in shifting and producing uncertainty in projected extreme flow. Among them, the climate model's discrepancy and hydrological models' structure are the more dominant source of uncertainty in projection of extreme high and low (or mean) flow in most of the regions, respectively. However, the quality of input data and hydrological model structures are the most dominant source of uncertainty in Africa, Latin America, and some parts of Asia. This indicated that these regions have strong hydrological cycle and higher physiographic heterogeneity. We believe that our existing knowledge and skills need to be improved and transformed into an accurate mathematical and physical representation, to minimize the uncertainty due to the effect of choices in the methodology chain. So, disentangling the aggregated uncertainty in the cascade modeling chain can be done by using variances, which can help understand the interaction effect and identify their contribution to the projected extreme flow. This comprehensive review can help modelers to identify and reduce uncertainty in projecting hydrological extremes and policy makers for full awareness of the various uncertainties to make a robust decision for water resources management under climate change. Article Highlights: This study provides a comprehensive review on reducing uncertainty in projecting hydrological extremes from the perspective of an integrated modeling framework Existing knowledge and skills need to be improved and transformed into an accurate mathematical representation The impact of climate change on hydrological extremes is highly variable and uncertain in space and time Uncertainties of the integrated modeling framework can be reduced by decomposing the aggregated uncertainty using variance analysis and/or the modeling chain [ABSTRACT FROM AUTHOR]

Big sagebrush ecosystems are widespread across drylands of western North America and provide numerous services, but the abundance of these ecosystems has declined substantially and the future of these ecosystems is uncertain. As a result, characterizing potential areas for expansion of these ecosystems is important. Species distribution models of the big sagebrush suggest areas of increasing climatic habitat suitability at northern latitudes under climate change scenarios. This implies the formation of a leading edge during a future big sagebrush range expansion. Such an expansion requires that current nearby range margin big sagebrush populations are stable and serve as future seed sources. Our goal was to quantify the impacts of future climate conditions on the plant community composition and biomass in the in range margin big sagebrush plant communities adjacent to the leading edge. We did this using an individual-based soil water and plant growth simulation model, STEPWAT2. We assessed community dynamics throughout the twenty-first century using 13 climate models under two representative concentration pathways to capture the variability among projections. Our results show minimal overall change in plant community composition and little change in biomass, suggesting that range margin big sagebrush plant communities adjacent to the leading edge will remain stable to serve as essential dispersal sources for future range expansion, assuming no other relevant changes such as changes in disturbance regimes. These assessments of plant community responses to shifts in climate and characterization of variability in future projections will help inform conservation planning and management of the big sagebrush ecosystem. [ABSTRACT FROM AUTHOR]

In this study, the performance of the latest high-resolution CORDEX-CORE model simulations is assessed with respect to the corresponding gridded Indian Meteorological Department (IMD) and ERA5 observations in representing the monsoon rainfall over northeast India during the historical period (1979–2005). Three different RCM model simulations (COSMO, RegCM4.7, and REMO) downscaled from the global data over the South Asian CORDEX domain are used in this study. Their corresponding RCM ensembles using boundary conditions from ERA-Interim re-analysis and CMIP5 GCMs and a combined ensemble of all the RCMs were also evaluated to assess their performance. The analysis shows that the COSMO model experiments resemble closer to both the observations while comparing with the other two RCMs. With respect to IMD and ERA5 observations, the COSMO model experiments show moderate wet bias across the study area. In contrast, the RegCM model experiments show very wet bias and the REMO model experiments show more dry bias across a significant part of the study region. Quantitatively, in comparison to IMD, the COSMO suite of models shows a slight overestimation ranging between 7 and 13%, and with ERA5, an underestimation of about 14 to 18% is observed. In comparison to both observations, the REMO model experiments underestimate (15 to 50%), whereas the RegCM model experiments overestimate (15 to 80%) the monsoon rainfall. Overall, these model experiments replicate the monsoon rainfall over the study region but with biases that differ spatially. [ABSTRACT FROM AUTHOR]

Hybrid desiccant cooling system has a higher electric coefficient of performance than that of electric heat pump system because evaporative cooler highly contributes to diminishing the cooling load. An abundant waste heat must be secured because of a thermal requirement for regenerating a desiccant rotor. However, the cooling capability of the evaporative cooler is not necessarily proportional to the amount of supplied heat. The optimized regeneration temperature of desiccant cooling system is acquired to maximize the cooling performance of the evaporative cooler. The hybrid desiccant cooling system model developed in our previous study was simulated to determine the optimized operating conditions under actual climate conditions of the summer season worldwide. The optimal value of temperature for desiccant rotor regeneration and the process air flow rate, those can minimize the electric power consumption, are obtained as a based on the outdoor absolute humidity and outdoor temperature. The effect of the room inlet temperature of the process air on the consumption rate of regeneration heat and electric power consumption is captured. The characteristics of the hybrid desiccant cooling system at the minimal and optimal regeneration temperatures were investigated and compared. [ABSTRACT FROM AUTHOR]

Air temperature and precipitation are the elements that most impact the development of plants and are essential for good agricultural planning. Thus, it aimed to evaluate climate change in Maranhão of Brazil using the Thornthwaite (Geographical Review 38:55–94, 1948) climate classification. It used historical series of precipitation and air temperature between 1980 and 2016, obtained by the National Institute of Meteorology of Brazil (INMET). Reference evapotranspiration (ETo) was estimated using the Thornthwaite (Geographical Review 38:55–94, 1948) method and thus calculated the water balance by Thornthwaite and Mather (Laboratory of Climatology 8:104) for all locations in the studied state. Thornthwaite (Geographical Review 38:55–94, 1948) moisture index was used to classify localities to their level of humidity or aridity. We used the scenarios RCP-2.6, RCP-4, RCP-6, and RCP-8.5 for analyzing twenty-first-century projections (2041–2060 and 2061–2080 periods). The average values of air temperature, rainfall, and reference evapotranspiration in the state of Maranhão were 27.30(± 0.53) °C, 1678.52 (± 377.70) mm, and 1067.98 (± 73.36) mm, respectively. The average annual climatic characterization of the state of Maranhão was 53.4 (± 38.6) mm of the soil water storage, 698.71 (81.07) mm of the water surplus, and 395.85 (± 65.91) mm of the water deficit. Maranhão had seven climatic indexes. The most prevalent in the state was the Humid (B1, B2, B3, and B4), with 61% of the entire territory. RCP2.6 and RCP 8.5 are the driest. RCP6.0 and RCP8.5 show the most significant changes in the current situation. The humidity index's reduction will harm various economic activities in Maranhão, such as agriculture, livestock, and fishing. [ABSTRACT FROM AUTHOR]

Climate change and reduction in nutrient loads have significant effects on primary production and phytoplankton growth dynamics. Since in the last few decades in many regions, nutrients in lakes were reduced simultaneously as the climate changed. Yet, it remains unclear which of the two has impacted primary production the most. In this study, we couple the General Ocean Turbulence Model with the Ecological Regional Ocean Model to disentangle the effects of climate change and reoligotrophication on primary production (PP) in Lake Geneva, Switzerland–France. We apply a data assimilation method to calibrate the model with the observations from the past (1981–1990) and validate it against the in situ data from the present decade (2011–2019). Both decades represent different climate conditions and trophic states of the lake. We show that the model is skilful to reproduce assimilated and unassimilated observations from both periods. According to our results, the effect of reoligotrophication on PP is marginally higher than that of warming, leading to a net decrease in primary production by 10% from the past to the present. The areal phosphorus supply in Lake Geneva, in spite of a decrease by ∼ 70%, is still characteristic of a meso-to-eutrophic ecosystem. This points towards an incomplete reoligotrophication of the lake. The effects of future climate change on winter mixing and PP dynamics have also been studied. Although there would be a significant reduction in deep mixing, the autotrophic production in Lake Geneva is expected to increase by ∼ 20% by the end of twenty-first century, largely due to stimulation in biomass build-up of temperature-dependent algae (e.g. dinoflagellates and cyanobacteria). Considering our results to represent other large temperate lakes with similar trophic status and water residence time as Lake Geneva, future climate scenarios are expected to bring back symptoms of eutrophication. [ABSTRACT FROM AUTHOR]

In this study, we examined the performance of 33 CMIP5 models to simulate summer monsoon rainfall over India for the century-long period 1901–2005. If the standardized rainfall anomaly from the long-term mean is less than –1, it is defined as a deficit summer monsoon season; moreover, for comprehensive analysis, we used moderate and severe deficit summer monsoons. Models are in three categories to capture the annual cycle of rainfall: (i) correct phase and amplitude (peak), (ii) phase with lower amplitude, and (iii) both failed. Many models are unable to mimic the link between subseasonal and seasonal rainfall, which is maximum for the July–August (JA) and June through September (JJAS) rainfall. Except for three models which overestimate the mean intensity of seasonal deficit monsoons, 24 models simulate the composite geographical pattern pretty well for moderate deficit monsoons. Twenty-five models are well represented/simulated in the category of extreme deficit monsoons. The MME simulates extremely dry deficit monsoons over India, with the out-of-phase feature of India's easternmost regions effectively captured. As two potential forcings of Indian Summer Monsoon Rainfall (ISMR), we examine El-Niño Southern Oscillation (ENSO) and Eurasian Snow. A large majority of deficit monsoons are attributed to factors other than El-Niño and Eurasian Snow in all four categories of model simulations. Models that can accurately recreate the distinguishing properties of deficit monsoons could be used to project future changes in the frequency, intensity, and spatial distribution of rainfall associated with extreme deficits. Research highlights: In CMIP5, 10 models simulate the annual cycle of rainfall across India reasonably well, while 18 models simulate a dry bias. Subseasonal rainfall correlations (JJAS-JJ, JJAS-JA, and JJAS-AS) depict majority of the models simulates correlations of JJ rainfall with seasonal JJAS rainfall against the observed whereas, observations demonstrate JA rainfall is outstandingly related with the seasonal periodic rainfall. With the exception of three models that overestimate the mean intensity of seasonal deficit monsoons, 24 models for moderate deficit monsoons closely resemble the composite spatial pattern. The MME simulates extremely powerful deficit monsoons over India, with the out-of-phase feature of India's easternmost regions effectively captured. For the Indian Territory, CMIP5 models can replicate less-frequent but more-intense deficit monsoons. [ABSTRACT FROM AUTHOR]

The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000–2015 at two different horizontal resolutions (0.44° and 0.11°). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11° resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model. [ABSTRACT FROM AUTHOR]

Climate change may cause adverse impact on agricultural production that could jeopardize food availability and security. In this paper, we investigate how changes in mean values and variability of weather variables may affect rice yield using panel data for 30 districts of Tamil Nadu State from 1971 to 2018. We estimate a fixed-effects regression model with panel-corrected standard errors. Results show that rainfall and temperature have a statistically significant impact on rice yield. Furthermore, weather variability, measured as standard deviations of temperature and rainfall, has a negative effect on rice yields. We use the Coupled Model Inter-comparison Project-5 outputs and dynamically downscaled the weather outputs using the regional climate model. The results of the climate model showed that the rise in temperature and disruptions in rainfall patterns including both excessive and deficient rainfall events in Tamil Nadu will continue in the future under different climate scenarios. Projected changes in the weather variables are likely to decrease rice yield in Tamil Nadu from 0.7 to 6.3% under the low emission scenario and 4.1 to 20.1% in the high emission scenario during 2022–2050 (relative to 1971–2018). These projections have implications for the planning and targeting of climate adaptation technologies such as drought-tolerant and flood-tolerant varieties to lessen the adverse impact of weather variability due to climate change in the future. [ABSTRACT FROM AUTHOR]