Mean Near-surface Air Temperature Research Articles

Modeled temperature, mortality impact and external benefits of cool roofs and rooftop photovoltaics in London

Population exposure to high temperatures poses health risks and increases mortality. ‘Cool roofs’ (high-albedo roofs) and rooftop photovoltaics (RPV) may reduce temperatures in urban areas. Here, using advanced urban climate modeling, we model impacts of these measures on air temperature and heat-related mortality in London during the record-breaking hot summer of 2018. We estimate changes in mean near-surface air temperature of −0.3 °C in the RPV scenario and −0.8 °C in the cool roof scenario. We find that the heat-related mortality in this period (estimated 655–920) could have been reduced by 96 (12%) by RPV, or 249 (32%) by cool roofs, in scenarios where all roofs have these measures. Monetized using value of statistical life, we estimate benefits for RPV and cool roofs of £237 M and £615 M, respectively. We estimate that up to 20 TWh of electrical energy would be generated in the full RPV scenario. We show that, for conditions such as in London June–August 2018, RPV or cool roofs may reduce near-surface air temperatures and associated heat-related mortality, with cool roofs having a larger effect.

Combining mathematical models and machine learning algorithms to predict the future regional-scale actual transpiration by maize

Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.

Brief communication: Surface energy balance differences over Greenland between ERA5 and ERA-Interim

Abstract. We compare the main atmospheric drivers of the melt season over the Greenland Ice Sheet (GrIS) in ERA5 and ERA-Interim (ERAI) in their overlapping period 1979–2018. In summer, ERA5 differs significantly from ERAI, especially in the melt regions. Small-scale ERA5 − ERAI differences near the ice sheet's margins and over steep slopes can be explained by the different resolution, while the large-scale differences indicate a different representation of physical processes in the two reanalyses: averaged over the lower parts of the GrIS, the mean near-surface air temperature is 1 K lower, while the mean downward shortwave radiation at the surface is on average 15 W m−2 higher than in ERAI. Comparison with observational weather station data shows a significant warm bias in ERAI and, for ERA5, a significant positive bias in downward shortwave radiation. Consequently, methods that previously estimated the GrIS surface mass balance from the ERAI surface energy balance need to be carefully recalibrated before converting to ERA5 forcing.

Optimal Fingerprinting with Estimating Equations

Abstract Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951–2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses. Significance Statement Optimal fingerprinting is an important statistical tool for estimating human influences on the climate and for quantifying the associated uncertainty. Nonetheless, the estimators from the prevailing practice are not as optimal as believed, and their uncertainties are underestimated, both owing to the unreliable estimation of the optimal weight matrix that is critical to the method. Here we propose an estimation method based on the theory of estimating equations; to assess the uncertainty of the resulting estimator, we propose a pseudo bootstrap procedure. Through extensive numerical studies commonly used in statistical investigations, we demonstrate that the new estimator has a smaller mean-square error, and its uncertainty is estimated much closer to the true uncertainty than the prevailing total least squares method.

Glacier fluctuations and a proglacial evolution in King George Bay (King George Island), Antarctica, since 1980 decade.

This study aims to investigate the glacier shrinkage and recent proglacial environment in King George Bay, Antarctica, since 1988 in response to climate change. Remote sensing data (SPOT, Sentinel, Landsat and Planet Scope images) were applied to glacial landforms and ice-marginal fluctuations mapping. Annual mean near-surface air temperature reanalysis solutions from ERA-Interim were analyzed. Moraines and glaciofluvial landforms were identified. The Ana Northern Glacier has the highest retreat value (3.64 km) (and area loss of 31%) in response to higher depth in frontal ice-margin and reveal ocean-glacier linkages. The Ana South Glacier changed from a tidewater glacier to land-terminating after 1995, and had an outline minimum elevation variation of 89 meters, a shrinkage of 0.63 km, and a new proglacial subaerial sector. The Ana South Glacier foreland had recessional moraines (probably formed between 1995 and 2022), lagoons, and lakes. There are many flutings in low-relief environments. The 1980-1989, 1990-1999, 2000-2009, 2010-2019 anomaly plots concerning to the 1980-2019 average for atmospheric temperature, are shown to be a driver of the local glacial trends.

Observations of Climate Change Impacts on Terrestrial Ecosystems in Russia

In Russia, the modern climate change manifests primarily as substantially increasing annual mean near-surface air temperature. Its rate is 2-2.5 times higher than for globe. The amount of precipitation in the most parts of the country is also growing, but the pattern is more heterogeneous. The annual number of dangerous hydrometeorological events causing damage has also increased by 2-3 times compared to the end of the 20th century. The following adverse events for terrestrial ecosystems are indicated in the literature most often: increase in seasonal temperatures, heat and cold waves, permafrost degradation, droughts and aridization, floods, hurricanes, dust storms, natural fires, coastal erosion, mudflows, landslides, avalanches, the invasion of alien species, an increase in outbreaks of pests and diseases. They manifest either as trends or as hazards. In this paper, occurrence of a negative event within the natural zone is verified using meteorological databases and special reports of Russian Hydrometeorological Service. Dangerous events are distributed unevenly throughout the country. For all zonal ecosystems (polar deserts, tundra, boreal forests, broadleaved forests, subtropical forests, steppes, deserts) and mountain ecosystems, the number of types for hazardous events exceeds the number of trends. For rivers and lakes, their number turned out to be equal, and for peatlands, the number of unfavorable trends turned out to be higher than dangerous events. The least number of negative trends is observed in deserts (2). In other types of ecosystems, 3-4 negative trends may appear simultaneously. The biggest number of hazards (9) is found in mountains, and almost the same number (8) in tundra, taiga and steppes. The minimum variety of considered hazardous phenomena is noted in peatlands.

Agricultural management effects on mean and extreme temperature trends

Abstract. Understanding and quantifying land management impacts on local climate is important for distinguishing between the effects of land management and large-scale climate forcings. This study for the first time explicitly considers the radiative forcing resulting from realistic land management and offers new insights into the local land surface response to land management. Regression-based trend analysis is applied to observations and present-day ensemble simulations with the Community Earth System Model (CESM) version 1.2.2 to assess the impact of irrigation and conservation agriculture (CA) on warming trends using an approach that is less sensitive to temperature extremes. At the regional scale, an irrigation- and CA-induced acceleration of the annual mean near-surface air temperature (T2m) warming trends and the annual maximum daytime temperature (TXx) warming trends were evident. Estimation of the impact of irrigation and CA on the spatial average of the warming trends indicated that irrigation and CA have a pulse cooling effect on T2m and TXx, after which the warming trends increase at a greater rate than the control simulations. This differed at the local (subgrid) scale under irrigation where surface temperature cooling and the dampening of warming trends were both evident. As the local surface warming trends, in contrast to regional trends, do not account for atmospheric (water vapour) feedbacks, their dampening confirms the importance of atmospheric feedbacks (water vapour forcing) in explaining the enhanced regional trends. At the land surface, the positive radiative forcing signal arising from enhanced atmospheric water vapour is too weak to offset the local cooling from the irrigation-induced increase in the evaporative fraction. Our results underline that agricultural management has complex and non-negligible impacts on the local climate and highlight the need to evaluate the representation of land management in global climate models using climate models of higher resolution.

Effects of Dynamic Vegetation on Global Climate Simulation Using the NCEP GFS and SSiB4/TRIFFID

Two global experiments were carried out to investigate the effects of dynamic vegetation processes on numerical climate simulations from 1948 to 2008. The NCEP Global Forecast System (GFS) was coupled with a biophysical model, the Simplified Simple Biosphere Model (SSiB) version 2 (GFS/SSiB2), and it was also coupled with a biophysical and dynamic vegetation model, SSiB version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) (GFS/SSiB4/TRIFFID). The effects of dynamic vegetation processes on the simulation of precipitation, near-surface temperature, and the surface energy budget were identified on monthly and annual scales by assessing the GFS/SSiB4/TRIFFID and GFS/SSiB2 results against the satellite-derived leaf area index (LAI) and albedo and the observed land surface temperature and precipitation. The results show that compared with the GFS/SSiB2 model, the temporal correlation coefficients between the globally averaged monthly simulated LAI and the Global Inventory Monitoring and Modeling System (GIMMS)/Global Land Surface Satellite (GLASS) LAI in the GFS/SSiB4/TRIFFID simulation increased from 0.31/0.29 (SSiB2) to 0.47/0.46 (SSiB4). The correlation coefficients between the simulated and observed monthly mean near-surface air temperature increased from 0.50 (Africa), 0.35 (Southeast Asia), and 0.39 (South America) to 0.56, 0.41, and 0.44, respectively. The correlation coefficients between the simulated and observed monthly mean precipitation increased from 0.19 (Africa), 0.22 (South Asia), and 0.22 (East Asia) to 0.25, 0.27, and 0.28, respectively. The greatest improvement occurred over arid and semiarid areas. The spatiotemporal variability and changes in vegetation and ground surface albedo modeled by the GFS with a dynamic vegetation model were more consistent with the observations. The dynamic vegetation processes contributed to the surface energy and water balance and in turn, improved the annual variations in the simulated regional temperature and precipitation. The dynamic vegetation processes had the greatest influence on the spatiotemporal changes in the latent heat flux. This study shows that dynamic vegetation processes in earth system models significantly improve simulations of the climate mean status.

Regional disparities and seasonal differences in climate risk to rice labour

The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term (p ≪ 0.01, R 2 > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress.

Polyphony of Short-Term Climatic Variations

It is widely accepted to believe that humanity is mainly responsible for the worldwide temperature growth during the period of instrumental meteorological observations. This paper aims to demonstrate that it is not so simple. Using a wavelet analysis on the example of the time series of the global mean near-surface air temperature created at the American National Climate Data Center (NCDC), some complex structures of inter-annual to multidecadal global mean temperature variations were discovered. The origin of which seems to be better attributable to the Chandler wobble in the Earth’s Pole motion, the Luni-Solar nutation, and the solar activity cycles. Each of these external forces is individually known to climatologists. However, it is demonstrated for the first time that responses of the climate system to these external forces in their integrity form a kind of polyphony superimposed on a general warming trend. Certainly, the general warming trend as such remains to be unconsidered. However, its role is not very essential in the timescale of a few decades. Therefore, it is this polyphony that will determine climate evolution in the nearest future, i.e., during the time most important for humanity currently.

Expansion of glacial lakes on Nelson and King George Islands, Maritime Antarctica, from 1986 to 2020

This study aims to quantify the areal variation of glacial lakes in the coastal ice-free areas of King George Island (KGI) and Nelson Island (NI) from 1986 to 2020. Glacial lake and glacial area fluctuations are estimated using spaceborne remote sensing data and annual mean near-surface air temperature data from station observations and ERA-Interim reanalysis were analyzed. The area of glacial lakes expanded 732%, from 0.18 km2 in the 1980s to 1.39 km2 in 2020. For the 1988–2000/2003 and 2000/2003–2020 periods, the increase in NI lake area is estimated to be 190% and 86%, respectively. Meanwhile, on KGI, the increase is estimated to be 316% for the 1988–2000/2003 period and 103% for the 2000/2003–2020 period. The majority of lakes and higher lake expansions occurred at the glacier fronts and can be attributed to the melting of glacier fronts and the subsequent glacial retreat in recent decades.

Constraining human contributions to observed warming since the pre-industrial period

Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contributions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demonstrate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals.

How large does a large ensemble need to be?

Abstract. Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool for quantifying the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean near-surface air temperature, local temperature and precipitation, and variability in the El Niño–Southern Oscillation (ENSO) region and central United States for the Max Planck Institute Grand Ensemble (MPI-GE). Estimating the required ensemble size is relevant not only for designing or choosing a large ensemble but also for designing targeted sensitivity experiments with a model. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. We show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity.

A Synergistic Effect of Blockings on a Persistent Strong Cold Surge in East Asia in January 2018

A persistent strong cold surge occurred in East Asia in late January 2018, causing mean near-surface air temperature in China to hit the second lowest since 1984. Moreover, the daily mean air temperature remained persistently negative for more than 20 days. Here, we find that a synergistic effect of double blockings in Western Europe and North America plays an important accelerating role in the rapid phase transition of Arctic Oscillation and an amplifying role in the strength of cold air preceding to the cold surge outbreaks by the use of an isentropic potential vorticity analysis. In mid-January, an Atlantic mid-latitude anticyclone merged with Western Europe blocking, which led to a strengthening of the blocking. Simultaneously, the Pacific-North American blocking was also significantly strengthened. The two blockings synchronously deeply stretched towards the Arctic, which resulted in, on the one hand, warm and moist air of the Pacific and the Atlantic being excessively transported into the Arctic, and on the other hand, the polar vortex being split and cold air being squeezed southwards and accumulating extensively on the West Siberian Plain. After the breakdown of the double blocking pattern, which lasted for about 10 days, the record-breaking cold surge broke out in East Asia. It was discovered that the synergistic effect of double blockings extending into the Arctic, which is conducive to extreme cold events, has been rapidly increasing in recent years.

Meridional Distributions of Historical Zonal Averages and Their Use to Quantify the Global and Spheroidal Mean Near-Surface Temperature of the Terrestrial Atmosphere

The zonal averages of temperature (the so-called normal temperatures) for numerous parallels of latitude published between 1852 and 1913 by Dove, Forbes, Ferrel, Spitaler, Batchelder, Arrhenius, von Bezold, Hopfner, von Hann, and Börnstein were used to quantify the global (spherical) and spheroidal mean near-surface temperature of the terrestrial atmosphere. Only the datasets of Dove and Forbes published in the 1850s provided global averages below 〈T〉=14°C, mainly due to the poor coverage of the Southern Hemisphere by observations during that time. The global averages derived from the distributions of normal temperatures published between 1877 and 1913 ranged from 〈T〉=14.0°C (Batchelder) to 〈T〉=15.1°C (Ferrel). The differences between the global and the spheroidal mean near-surface air temperature are marginal. To examine the uncertainty due to interannual variability and different years considered in the historic zonal mean temperature distributions, the historical normal temperatures were perturbed within ±2σ to obtain ensembles of 50 realizations for each dataset. Numerical integrations of the perturbed distributions indicate uncertainties in the global averages in the range of ±0.3°C to ±0.6°C and depended on the number of available normal temperatures. Compared to our results, the global mean temperature of 〈T〉=15.0°C published by von Hann in 1897 and von Bezold in 1901 and 1906 is notably too high, while 〈T〉=14.4°C published by von Hann in 1908 seems to be more adequate within the range of uncertainty. The HadCRUT4 record provided 〈T〉≌ 13.7°C for 1851-1880 and 〈T〉=13.6°C for 1881-1910. The Berkeley record provided 〈T〉=13.6°C and 〈T〉≌ 13.5°C for these periods, respectively. The NASA GISS record yielded 〈T〉=13.6°C for 1881-1910 as well. These results are notably lower than those based on the historic zonal means. For 1991-2018, the HadCRUT4, Berkeley, and NASA GISS records provided 〈T〉=14.4°C, 〈T〉=14.5°C, and 〈T〉=14.5°C, respectively. The comparison of the 1991-2018 globally averaged near-surface temperature with those derived from distributions of zonal temperature averages for numerous parallels of latitude suggests no change for the past 100 years.

Efficacy of Climate Forcings in PDRMIP Models.

Quantifying the efficacy of different climate forcings is important for understanding the real‐world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near‐surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy‐induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.

A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.

Multi-annual modes in the 20th century temperature variability in reanalyses and CMIP5 models

Abstract. A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901–2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.

Spatio-temporal changes of precipitation and temperature over the Pearl River basin based on CMIP5 multi-model ensemble

Projections of changes in climate are important in assessing the potential impacts of climate change on natural and social systems. However, current knowledge on assembling different GCMs to estimate future climate change over the Pear River basin is still limited so far. This study examined the capability of BMA and arithmetic mean (AM) method in assembling precipitation and temperature from CMIP5 under RCP2.6, RCP4.5 and RCP8.5 scenarios over the Pearl River basin. Results show that the BMA outperforms the traditional AM method. Precipitation tends to increase over the basin under RCP2.6 and RCP4.5 scenarios, whereas decrease under RCP8.5. The most remarkable increase of precipitation is found in the northern region under RCP2.6 scenario. The linear trend of the monthly mean near-surface air temperature increases with the growing CO2 concentration. The warming trends in four seasons are distinct. The warming rate is prominent in summer and spring than that in other season, meanwhile it is larger in western region than in other parts of the basin. The findings can provide beneficial reference to water resources and agriculture management strategies, as well as the adaptation and mitigation strategies for floods and droughts under the context of global climate change.

Validation of the ALARO-0 model within the EURO-CORDEX framework

Abstract. Using the regional climate model ALARO-0, the Royal Meteorological Institute of Belgium and Ghent University have performed two simulations of the past observed climate within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The ERA-Interim reanalysis was used to drive the model for the period 1979–2010 on the EURO-CORDEX domain with two horizontal resolutions, 0.11 and 0.44°. ALARO-0 is characterised by the new microphysics scheme 3MT, which allows for a better representation of convective precipitation. In Kotlarski et al. (2014) several metrics assessing the performance in representing seasonal mean near-surface air temperature and precipitation are defined and the corresponding scores are calculated for an ensemble of models for different regions and seasons for the period 1989–2008. Of special interest within this ensemble is the ARPEGE model by the Centre National de Recherches Météorologiques (CNRM), which shares a large amount of core code with ALARO-0. Results show that ALARO-0 is capable of representing the European climate in an acceptable way as most of the ALARO-0 scores lie within the existing ensemble. However, for near-surface air temperature, some large biases, which are often also found in the ARPEGE results, persist. For precipitation, on the other hand, the ALARO-0 model produces some of the best scores within the ensemble and no clear resemblance to ARPEGE is found, which is attributed to the inclusion of 3MT. Additionally, a jackknife procedure is applied to the ALARO-0 results in order to test whether the scores are robust, meaning independent of the period used to calculate them. Periods of 20 years are sampled from the 32-year simulation and used to construct the 95 % confidence interval for each score. For most scores, these intervals are very small compared to the total ensemble spread, implying that model differences in the scores are significant.