Annual Mean Surface Air Temperature Research Articles

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Anthropogenic fingerprints in daily precipitation revealed by deep learning

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1–4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

Urbanization Heat Flux Modeling Confirms It Is a Likely Cause of Significant Global Warming: Urbanization Mitigation Requirements

Recent ground-based measurements find the magnitude of the urbanization effect on the global average annual mean surface air temperature corresponds to an urbanization contribution of 12.7%. It is important to provide modeling to help understand these results as there are conflicting concerns. This study models the global warming contribution that urbanization heat fluxes (UHF) can make due to anthropogenic heat release (AHR), and solar heating of impermeable surface areas (ISAs), with additional secondary effects. Results help explain and support ground-based observations. Climate models typically omit anthropogenic heat release (AHR) as warming estimates are below 1%. In agreement, the baseline assessment in this paper has similar findings. However, in this study, the methods of climate amplification estimates (MCAE) with data-aided physics-based amplification models are used. When the MCAE are applied at the global and microclimate levels that consider greenhouse gases (GHGs), feedback, and other secondary effects; the results show that AHR fluxes can amplify, increasing to have an estimated global warming (GW) influence of 6.5% from 1950 to 2022 yielding a 0.9% decade−1 increase. This increasing rate due to energy consumption is found as anticipated to be reasonably correlated to the increasing population growth rate over this time. Furthermore, using the MCAEs, this paper studies heat fluxes assessment due to solar heating of unshaded impermeable surfaces including likely secondary amplification effects. Impermeable surface areas (ISAs) such as asphalt roads, roofs, and building sides have been reported with high land surface contact temperatures (LSCTs) relative to non-ISAs and significantly found to contribute to urbanization warming. Results indicate that high-temperature unshaded impermeable surfaces (including building sides) are estimated to average around 10–11 °C above the earth’s ambient temperature of 14.5 °C (showing albedo ISA estimates between 0.133 and 0.115 respectively); the ISA heat fluxes with secondary effects are estimated to have about a GW influence of 6.5%. This is broken down with average contributions of 4.0% from urban ISAs and 2.5% from rural ISA heat fluxes. Asphalt road ISA heat fluxes are estimated to have about a 1.1% global warming influence. Then the total UHF effect from ISAs and AHR with secondary effects is assessed in modeling to yield a combined average GW influence of 13% helping to confirm ground-based measurement results. Several key adjustment values were used for shading, cloud coverage, and rural-to-urban ISA ratios. Microclimate GHGs and related water vapor feedback (WVF) were assessed to increase urban warming by about 50%. As well an assessment of water vapor and radiation increases from UHF is provided. This study also shows the need to incorporate urbanization heat fluxes with secondary effects into climate models and indicates the necessity for Paris Agreement urban heat flux mitigation goals. Results also found that given average climate conditions, it is possible to mitigate much of the UHI effect with an albedo increase of 0.1 that is anticipated to lower the average impermeable surface temperatures by about 9 °C. Studies show this can be accomplished with cost-effective cool roads and roofs. Although roads are only estimated to occupy 14% of ISAs, changing roads from asphalt to concrete-type surfaces would improve reflectivity by about a factor of 5 and is estimated to mitigate about 5.5% of global warming. Unfortunately, the current overuse of black asphalt on pavements and roofs is highly dangerous to our environment causing UHI increases in heatwaves, excessive temperatures, and global warming issues and should be banned. Asphalt usage also reduces opportunities for solar geoengineering of urbanization.

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.

High-resolution climate projection over the Tibetan Plateau using WRF forced by bias-corrected CESM

The Tibetan Plateau (TP) has undergone significant climate warming with a stronger amplitude than that experienced elsewhere in the Northern Hemisphere during the past years, but it is still challenging for most regional climate models to realistically simulate the present-day climate and promisingly project the future climate over the TP. In this study, high-resolution simulation using the Weather Research and Forecasting model (WRF) driven by bias-corrected CESM is conducted from 1979 to 2100, with the period from 2006 to 2100 under RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios. The simulated present-day climate is evaluated firstly and then the future climate is studied secondly. The results show that compared with station observation, WRF successfully captures the spatial pattern of annual mean surface air temperature (T2m) and precipitation over the TP, with the spatial correlation coefficients larger than 0.95 for T2m and larger than 0.70 for precipitation. However, great underestimation of T2m over the southeastern TP is found in the cold season which is related to the underestimation of snow there, and the snow-temperature positive feedback develops. WRF shows limited ability in reducing the dry bias in summer, which is related to the simulated weaker water vapor transport over the southern and eastern TP. For the future changes, substantial warming, general increase in precipitation and decrease in snow are projected under RCP8.5. The warming magnitude is greater over the western TP where the more significant decrease of snow will occur by the end of 21st century. Projected precipitation tends to consistently decrease over the western TP and along the south flank of TP which is related to the low-level circulation change. The occurring frequency of light precipitation will decrease while that of non-precipitation and extreme precipitation will increase especially in the far future, with the more obvious change occurring under RCP8.5. Overall, WRF shows high ability in simulating the present-day climate and thus reliable performance in future climate projection.

WRF gray-zone dynamical downscaling over the Tibetan Plateau during 1999–2019: model performance and added value

The Tibetan Plateau (TP) is an important component of the global climate system, while the characteristics of its climate are poorly represented in most regional climate models at coarse resolutions. In this study, a 20-year (2000–2019) dynamical downscaling simulation at the gray-zone resolution (9 km) using the WRF model driven by the ERA5 reanalysis is conducted over the TP. Based on comparisons against in-situ observations and the Integrated Multi-satellite Retrievals for GPM (IMERG) version 6 satellite precipitation product, the assessment of basic climate variables, such as near-surface air temperature (T2m) and precipitation, is performed to evaluate the model’s performance and understand its added value better. Results show that both WRF and ERA5 can successfully reproduce the spatial patterns of annual mean and seasonal mean surface air temperature. However, significant cold and wet biases are found especially over the southeastern TP in ERA5, which are greatly improved in WRF with reduced RMSEs. Not only the climatological characteristics, but also the inter-annual variability and seasonal variation of T2m and precipitation are well captured by WRF which reduces the cold and wet biases especially in winter and summer compared to ERA5, respectively. Besides, at daily scale, the overestimation of precipitation in WRF and ERA5 is mainly caused by the overestimated precipitation frequency when precipitation intensity changed slightly. Furthermore, WRF outperforms ERA5 in capturing the diurnal variation of precipitation with more realistic peak time in all sub-regions over the TP. Further investigation into the mechanism of model bias reveals that less simulated snow cover fraction plays a crucial role in increasing the surface net energy by affecting surface albedo over the southeastern TP in WRF, leading to higher T2m. In addition, less water vapor transport from the southern boundary of TP leads to reduced wet bias in WRF, indicating that the added value in dynamical downscaling at gray-zone resolution is obtained by representing water vapor transport more realistically.

A simulation study on the climatic responses to short-lived climate pollutants changes from the pre-industrial era to the present

Short-lived climate pollutants (SLCPs) including methane, tropospheric ozone, and black carbon in this work, is a set of compounds with shorter lifetimes than carbon dioxide (CO2) and can cause warming effect on climate. Here, the effective radiative forcing (ERF) is estimated by using an online aerosol–climate model (BCC_AGCM2.0_CUACE/Aero); then the climate responses to SLCPs concentration changes from the pre-industrial era to the present (1850–2010) are estimated. The global annual mean ERF of SLCPs was estimated to be 0.99 [0.79–1.20] W m−2, and led to warming effects over most parts of the globe, with the warming center (about 1.0 K increase) being located in the mid-high latitudes of the Northern Hemisphere (NH) and the ocean around Antarctica. The changes in annual mean surface air temperature (SAT) caused by SLCPs changes were more prominent in the NH [0.78 (0.62–0.94) K] than in the Southern Hemisphere [0.62 (0.45–0.74) K], and the global annual mean value is 0.70 K. By looking at other variable responses, we found that precipitation had been increased by about 0.10 mm d−1 in mid- and high-latitudes and decreased by about 0.20 mm d−1 in subtropical regions, with the global annual mean value of 0.02 mm d−1. Changes in SLCPs also influenced atmospheric circulation change, a northward shift in the Intertropical Convergence Zone was induced due to the interhemispheric asymmetry in SAT. However, it is found in this work that SLCPs changes had little effect on global average cloud cover, whereas the local cloud cover changes could not be ignored, low cloud cover increase by about 2.5% over high latitudes in the NH and the ribbon area near 60°S, and high cloud cover increased by more than 2.0% over northern Africa and the Indian Ocean. Finally, we compared the ERFs and global and regional warming effects of SLCPs with those induced by CO2 changes. From 1850 to the present, the ERF of SLCPs was equivalent to 66%, 83%, and 50% of that of CO2 in global, NH, and SH mean, respectively. The increases in SAT caused by SLCPs were 43% and 55% of those by CO2 over the globe and China, respectively.

A new global surface temperature reconstruction for the Last Glacial Maximum

Abstract. We present a new reconstruction of surface air temperature and sea surface temperature for the Last Glacial Maximum. The method blends model fields and sparse proxy-based point estimates through a data assimilation approach. Our reconstruction updates that of Annan and Hargreaves (2013), using the full range of general circulation model (GCM) simulations which contributed to three generations of the PMIP database, three major compilations of gridded sea surface temperature (SST) and surface air temperature (SAT) estimates from proxy data, and an improved methodology based on an ensemble Kalman filter. Our reconstruction has a global annual mean surface air temperature anomaly of -4.5±0.9 ∘C relative to the pre-industrial climate. This is slightly colder than the previous estimate of Annan and Hargreaves (2013), with an upwards revision on the uncertainty due to different methodological assumptions. It is, however, substantially less cold than the recent reconstruction of Tierney et al. (2020). We show that the main reason for this discrepancy is in the choice of prior. We recommend the use of the multi-model ensemble of opportunity as potentially offering a credible prior, but it is important that the range of models included in the PMIP ensembles represent the main sources of uncertainty as realistically and comprehensively as practicable if they are to be used in this way.

Surface air temperature changes over the Tibetan Plateau: Historical evaluation and future projection based on CMIP6 models

With its amplification simultaneously emerging in cryospheric regions, especially in the Tibetan Plateau, global warming is undoubtedly occurring. In this study, we utilized 28 global climate models to assess model performance regarding surface air temperature over the Tibetan Plateau from 1961 to 2014, reported spatiotemporal variability in surface air temperature in the future under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), and further quantified the timing of warming levels (1.5, 2, and 3 °C) in the region. The results show that the multimodel ensemble means depicted the spatiotemporal patterns of surface air temperature for the past decades well, although with differences across individual models. The projected surface air temperature, by 2099, would warm by 1.9, 3.2, 5.2, and 6.3 °C relative to the reference period (1981–2010), with increasing rates of 0.11, 0.31, 0.53, and 0.70 °C/decade under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios for the period 2015–2099, respectively. Compared with the preindustrial periods (1850–1900), the mean annual surface air temperature over the Tibetan Plateau has hit the 1.5 °C threshold and will break 2 °C in the next decade, but there is still a chance to limit the temperature below 3 °C in this century. Our study provides a new understanding of climate warming in high mountain areas and implies the urgent need to achieve carbon neutrality.

Transitions to new climates (TNCs) in the 21st century

We introduce the concept of transition to a new climate (TNC) based on ensembles of model projections. We consider a variable whose distribution due to interannual variability and inter-model spread of responses within a given time slice is measured by a certain compounded standard deviation, a TNC then occurs when the mean change signal of the variable between a future and a reference period exceeds the sum of the standard deviations for the two periods multiplied by a factor, taken here as 1.6 (see text). We calculate TNCs of regional mean annual surface air temperature from the CMIP6 ensemble of 21st century projections for 31 regions of the globe and four SSP scenarios. For the high-end scenarios, SSP5-8.5 and SSP3-7.0, we find the occurrence of at least one TNC in all regions and a second TNC in 15 and 10 regions, respectively, primarily located in tropical and mid-latitude regions and separated by about 40–45 years. For 30 out of 31 regions there is occurrence of a single TNC in the mid-level SSP2-4.5 scenario, while only 20 out of 31 regions experience a TNC in the low end SSP1-2.6. High latitude and polar regions tend to experience fewer and later occurring TNCs than low latitude ones, due to their larger interannual variability and inter-model response. On the one hand, the occurrence of at least one TNC, and in some scenarios and regions two TNCs, imply severe stress for adaptation of natural ecosystems and different socioeconomic sectors. On the other, the pronounced reduction of TNC occurrence in the low end scenarios point to the urgency of implementing effective mitigation policies to curb global warming.

Transient climate simulations of the Holocene (version 1) – experimental design and boundary conditions

Abstract. The Holocene, which started approximately 11.5 ka, is the latest interglacial period with several rapid climate changes with timescales, from decades to centuries, superimposed on the millennium-scale mean climate trend. Climate models provide useful tools to investigate the underlying dynamic mechanisms for the climate change during this well-studied time period. Thanks to the improvements in the climate model and computational power, transient simulation of the Holocene offers an opportunity to investigate the climate evolution in response to time-varying external forcings and feedbacks. Here, we present the design of a new set of transient experiments for the whole Holocene from 11.5 ka to the preindustrial period (1850; HT-11.5 ka) to investigate both the combined and separated effects of the main external forcing of orbital insolation, atmospheric greenhouse gas (GHG) concentrations, and ice sheets on the climate evolution over the Holocene. The HT-11.5 ka simulations are performed with a relatively high-resolution version of the comprehensive Earth system model CESM1.2.1 without acceleration, both fully and singly forced by time-varying boundary conditions of orbital configurations, atmospheric GHGs, and ice sheets. Preliminary simulation results show a slight decrease in the global annual mean surface air temperature from 11.5 to 7.5 ka due to both changes in orbital insolation and GHG concentrations, with an abrupt cooling at approximately 7.5 ka, which is followed by a continuous warming until the preindustrial period, mainly due to increased GHG concentrations. Both at global and zonal mean scales, the simulated annual and seasonal temperature changes at 6 ka lie within the range of the 14 PMIP4 model results and are overall stronger than their arithmetic mean results for the Middle Holocene simulations. Further analyses on the HT-11.5 ka transient simulation results will be covered by follow-up studies.

Ice Coverage of the Laptev Sea and Air Temperature Variation during Recent Centuries: Observed Data and Reconstructions Using a Geochemical Proxy

Objective: The aim was to reconstruct the climatic parameters and ice of the Laptev Sea over the last centuries and to identify the causes of their variability. Background: The ice-free water area of the Arctic Ocean has increased considerably during the summer–autumn period in recent decades, which may be attributed to several climatic and oceanographic factors. Methods: The ice-free period duration and mean annual surface air temperature were reconstructed using the transfer function technique to compare hydrometeorological data and the chemical composition of bottom sediments accumulated during the observation period. This approach was based on sub-millimeter scanning of sediments using synchrotron radiation X-ray fluorescence. The results revealed a specific feature of the variations in the mean annual air temperature over the last 400 years in the Laptev Sea region, whereby higher (up to positive) temperature anomalies, were observed during the Little Ice Age. Some discrepancies in the ice coverage and climate fluctuations were observed during the period from the end of the Little Ice Age to the beginning of the period of accelerated ice-cover degradation in the Arctic (1850–1980). These discrepancies can be caused by the beginning of global warming, which have disturbed the natural cyclicality of atmospheric processes, whereas natural variations in ice coverage were more stable. Conclusion: A joint analysis of the reconstructed variations in air temperature and the duration of the ice-free period revealed the influence of atmospheric processes on the ice conditions of the Laptev Sea.

Unevenly distributed CO2 and its impacts on surface energy balance

Atmospheric carbon dioxide (CO2) has affected global surface and atmospheric energy balance since the Industrial Revolution. The spatial distribution of CO2 concentrations derived from satellite observation are introduced into the coupled Beijing Normal University Earth System Model (BNU-ESM) to study the responses of temperature and surface energy balance to non-uniform CO2 distribution under the Representative Concentration Pathway 8.5 scenario to 2100. Global annual mean atmospheric CO2 concentrations are set to equal those specified in the standard uniform CO2 simulations with differences in spatial pattern and seasonal cycles amounting to tens of ppm. Relative to the uniform CO2 simulation, global annual mean surface air temperature increases by 0.44 ± 0.03 °C over 2071–2100 when forced with non-uniformly distributed CO2, in the Arctic by 1.63 ± 0.28 °C and by 0.67 ± 0.08 °C in northern midlatitudes. The non-uniform CO2 simulation increases global surface energy by 0.68 ± 0.06 W m−2, principally due to a 0.73 ± 0.08 W m−2 increase in net downward surface longwave flux. Surface energy balance increases by 0.33 ± 0.11 W m−2 (7.4%) averaged over oceans indicting a significant increase in ocean heat uptake. Surface energy budget averaged over the Arctic summer increases by 0.91 ± 0.68 W m−2 in the non-uniform CO2 simulation, which would have impact on sea ice extent and land ice melt rates with associated feedbacks. The increased surface energy fluxes and temperatures imply reduced time and emission space for greenhouse gases before running into scientific and politically flagged temperature limits.

Projections of surface air temperature and precipitation in the 21st century in the Qilian Mountains, Northwest China, using REMO in the CORDEX

Qilian Mountains (QM) is an important ecological security barrier in China and has been significantly affected by climate change, it is therefore of great importance and necessity to project its future climate change using high-resolution climate models because of mountainous areas in the QM and relatively few targeted simulation analyses. In this study, we used the simulations of the regional climate model REMO with 25 km spatial resolution, driven by three different global climate models (MPI-ESM-MR, NorESM1-M, and HadGEM2-ES), to evaluate how annual and seasonal mean surface air temperature and precipitation in the QM are likely to change for three future periods (2011–2040, 2041–2070, and 2071–2100) under two representative concentration pathways (RCP2.6 and RCP8.5). The REMO model, shows noticeable cold and wet biases compared to observations for the reference period (1971–2000) and air temperature simulation outperforms precipitation simulation. The REMO simulations exhibit a warm and wet centre around lake, indicating that the simulation are likely influenced by lake. Projections under RCP2.6 show regional warming reaching 1.74 °C during 2011–2100, characterized by an initial increase and a decrease afterwards. Under RCP8.5, air temperatures increase monotonously from 2011 to 2100, with a warming magnitude of 5.36 °C for 2071–2100 relative to 1971–2000. The overall change in regional-average annual precipitation is not evident during 2011–2100, with some increases or decreases in certain time periods. In the 2071–2100 both the strongest warming and precipitation increase are projected to occur in winter under both scenarios, while precipitation in summer and autumn is projected to decrease in the east of the QM for the three future periods. The results suggest that the QM is likely to experience drought conditions in warm seasons in the future, which could impact agricultural and livestock production.

Verification Data and the Skill of Decadal Predictions

The utility of a forecast depends on its skill as demonstrated by past performance. For most forecasts errors rapidly become large compared to uncertainties in the observation-based state of the system and, for this reason, it is usually deemed adequate to assess predictions against a single verification dataset. Eleven reanalyses and station-based analyses of annual mean surface air temperature are compared as are basic skill measures obtained when using them to verify decadal prediction hindcasts from the Canadian Centre for Climate Modelling and Analysis forecasting system. There are differences between reanalysis and station-based analyses which translate also into differences in basic skill scores. In an average sense, using station-based verification data results in somewhat better correlation skill. The spread between the locally best and worst scores is obtained for individual forecast ensemble members and for ensemble mean forecasts compared to individual analyses. The comparison of ensemble mean forecasts against different analyses can result in apparent skill differences, and using a “favorable” analysis for verification can improve apparent forecast skill. These differences may be more pertinent for longer time averages and should be considered when verifying decadal predictions and when comparing the skill of decadal prediction systems as part of a model intercomparison project. Either a particular analysis could be recommended by the decadal prediction community, if such could be agreed on, or the ensemble average of a subset of recent analyses could be used, assuming that ensemble averaging will act to average out errors.

Testing mean air temperature trends in southern Greece: A Bayesian approach

AbstractThis study investigates the variability and trends of the mean annual and seasonal surface air temperature in southern Greece, namely in Crete and in the islands of the southern Aegean Sea. The climatic data used are the mean monthly homogenized air temperature values of seven meteorological stations of the Hellenic Meteorological Service, for the period 1960–2004. A Bayesian trend analysis is performed by fitting alternative stochastic‐trend models based on the Log‐normal, the Normal, the Generalized Extreme Value and the Gumbel distributions. Posterior simulations results based on the best fitting model reveal the locations and time‐periods with the strongest and the weakest interannual trends for both mean and variance. Furthermore, a model comparison experiment based on Bayes Factors compares the above stochastic‐trend models with the corresponding ones with no‐time trends. The results are very significant since they reveal the locations and the seasons where interannual trends for both mean and variance have real grounds.

General characteristics of climate change over China and associated dynamic mechanisms during the Last Interglacial based on PMIP4 simulations

Investigation of climate change during the Last Interglacial (LIG, ~130–115 ka BP) may deepen our understanding of the response of climate to Earth's orbit. Here we examine climate change over China and associated mechanisms during the LIG using the lig127k experiments from the Paleoclimate Modeling Intercomparison Project Phase 4. We illustrate that annual mean surface air temperature over China decreases by 0.22 °C during the LIG relative to the preindustrial, with a colder climate in winter and spring, whereas a warmer climate in summer and autumn. Annual mean precipitation increases (up to 54%) over northern and southwestern China during the LIG, whereas it decreases (up to 11%) over the other parts. This pattern largely holds for precipitation change in summer during the LIG, but precipitation generally decreases in winter. According to the moisture budget equation, the nonlinear and horizontal dynamic terms contribute largely to the drier (wetter) conditions in summer over the south (north) of eastern China, whereas the horizontal dynamic term and the horizontal thermodynamic term is more important in regulating drier conditions over south and north of eastern China in winter, respectively. Furthermore, East Asian summer monsoon is intensified during the LIG manifested by anomalous southerly at 850 hPa, a westward shift of western North Pacific subtropical high, and poleward migration of East Asian westerly jet. East Asian winter monsoon also gets stronger during the LIG, with anomalous northerly at low-level troposphere, enhanced mid-tropospheric East Asian trough, and an equatorward shift of westerly jet at upper troposphere. Additionally, we note obvious data-model discrepancies in annual temperature change over China during the LIG, with cooling seen in the models but warming suggested by multiproxies. This highlights close collaboration between model and data communities in future to deepen our understanding of climate change during the LIG.

The analysis of annual and seasonal surface air temperature trends of southern and southeastern Bosnia and Herzegovina from 1961 to 2017

In some areas of the world, regional climate change is in good agreement with global climate change. This study brings new information about what defines climate change in the contact area of Adriatic Sea and Southeastern Europe, and conclusions are based on trend analysis of annual and seasonal temperatures in the southern and southeastern parts of Bosnia and Herzegovina. Trend analysis was applied on mean annual surface air temperatures, mean maximum temperatures, and mean minimum temperatures of all four seasons. This study used 4 meteorological stations: Livno, Bileća, Mostar, and Ivan Sedlo for 56 years. The main statistical method is the Mann-Kendall test. The total number of analysis is 48. A statistically significant positive trend was determined in 36 analysis, while in the rest of the tests this was not the case. The highest amount of temperature increase is present in the mean maximum summer air temperatures in Livno and Ivan Sedlo. Mean minimum autumn air temperatures had the smallest increase. Negative trend is present in the mean autumn surface air temperatures and mean maximum autumn temperatures. Using a geographical information system resulted in visualizing regional differences in the spatial distribution of isotherms. The study area has combined the influence of orography and maritime effects of the Adriatic Sea. Having in mind these results, the growing temperature has been recognized as a problem which needs more attention in Bosnia and Herzegovina. Unfortunately, official documents which propose economic adaptation on climate change in this country are not at a satisfactory level.

Changes in anthropogenic particulate matters and resulting global climate effects since the Industrial Revolution

AbstractIn order to quantify air pollution effects on climate change, we investigated the climate response associated with anthropogenic particulate matters (PMs) by dividing fine PM (PM2.5, particle size ≤2.5 μm) and coarse particulate matter (CPM, particle size >2.5 μm) in great detail in this work, with an aerosol‐climate coupled model. We find that the changes in PM2.5 and CPM are very different and thus result in different, even opposite effects on climate, especially on a regional scale. The column burden of PM2.5 increases globally from 1850 to the present, especially over Asia's southern and eastern parts, whereas the column concentration of CPM increases over high‐latitude regions and decreases over South Asia. The resulted global annual mean effective radiative forcing (ERF) values due to PM2.5 and CPM changes are −1.21 W·m−2 and −0.24 W·m−2, respectively. Increases in PM2.5 result in significant cooling effects on the climate, whereas changes in CPM produce small and even opposite effects. The global annual mean surface air temperature (SAT) decreases by 0.94 K due to PM2.5 increase. Coolings caused by increased PM2.5 are more apparent over Northern Hemisphere (NH) terrain and ocean at mid‐ and high latitudes. Increases in SATs caused by increased CPM are identified over high latitudes in the NH, whereas decreases are identified over mid‐latitude regions. Strong cooling due to increased PM2.5 causes a southward shift of the Intertropical Convergence Zone (ITCZ), whereas the Hadley circulation associated with CPM is enhanced slightly over both hemispheres, along with the weak movement of corresponding ITCZ. The global annual mean precipitation decreases by approximately 0.11 mm day−1 due to the increased PM2.5. Generally, PM2.5 concentration changes contribute more than 80% of the variation caused by all anthropogenic aerosols in ERF, SAT, cloud fraction, and precipitation.

The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations

Abstract. The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.