Comment on esd-2023-12

ABSTRACTClimate change affects mean and extreme temperature and precipitation, increasing the risk of related disasters. Assessing the performance of current models in simulating these aspects is therefore crucial to future projection and policymaking. Based on a large ensemble (LE; 30 members) of the Community Earth System Model (CESM‐LE), this study seeks to answer the following two questions: (1) Are the simulated trends of mean and extreme temperature and precipitation over China consistent with observations? (2) What is the range of model uncertainty due to internal variability? The results show that CESM‐LE can capture the trends of observed annul mean temperature and extremely low temperature days (ELD) over 16–26% of China, but has difficulty in reproducing the observed trends in extremely high temperature days (EHD). For precipitation, CESM‐LE demonstrates similar capability in reproducing the observed trends in annual mean precipitation and extreme precipitation days (EPD). For consecutive dry days (CDD), the decreasing trends over part of northwestern China can be captured, but the increasing trends over northern China cannot be reproduced by CESM‐LE. Regarding the uncertainty of the model resulting from natural internal variability, because all 30 ensemble members of CESM use the same model and external forcing, CESM‐LE's spread (uncertainty) is mainly due to internal variability. Larger uncertainty in simulated trends is found over northern China and the Tibetan Plateau for annual mean temperature, over northeastern China and the Huang‐Huai region for ELD, and over the Tibetan Plateau for EHD. The trends in annual mean precipitation show uniform uncertainty over nearly all of China, except for the northwest. Larger uncertainty trends appear over southern and northwestern China for EPD, and over the Tibetan Plateau and northern China for CDD.

In recent years, European summer heatwaves have strongly intensified due to rising anthropogenic emissions. While European summer heatwaves will continue to intensify due to the warming of summer temperatures, the effects of the changes in internal variability under global warming remain unknown. Employing five single-model initial-condition large ensembles, we find that the forced changes in internal variability are projected to intensify central and northern European summer heatwaves. Central and northern Europe will experience frequent moisture limitations, enhancing land-atmosphere feedback and increasing heatwave intensity and variability. In contrast, the forced changes in internal variability will contribute to weakening southern European summer heatwaves. Southern Europe is projected to face a more stable moisture-depleted environment that reduces extreme temperature variability and heatwave intensity. Our findings imply that while adaptation to increasing mean temperatures in southern Europe should suffice to reduce the vulnerability to increasing EuSHW intensity, in central and northern Europe adaptation to increased temperature variability will also be needed.

Potential linkages of extreme climate events with vegetation and large-scale circulation indices in an endorheic river basin in northwest China

Daily maximum/minimum temperature and precipitation data from 35 weather stations in Xinjiang during 1961-2010 were examined using kriging spatial analysis, linear tendency estimation, and correlation analysis. Temporal trends and spatial distribution patterns of extreme temperature and precipitation in this area were then analyzed using 12 extreme temperature and 7 extreme precipitation indices. The following results were obtained. 1) Over the past 50 years, extreme cold indices, excepting the monthly maximum temperature minimum value and monthly extreme minimum temperature, showed slight decreasing trends. These indices include the maximum number of consecutive frost days, icy days, cold-nighttime days, and cold-daytime days. 2) Extreme warm events generally showed significant increasing trends (P < 0.01), including the indices of summertime days, warm-nighttime days, warm-daytime days, monthly extreme maximum temperature, and monthly minimum temperature maximum value. 3) The spatial distributions of threshold values of extreme warm and cold events showed notable regional differences. A reducing trend of extreme cold events and an increase in extreme warm events has occurred mainly in northern Xinjiang. 4) For the past 50 years, six extreme precipitation indices, aside from consecutive dry days, showed significant increasing trends in Xinjiang (P < 0.05) and notable differences in spatial distribution. The increase in extreme precipitation events was more rapid at northern than at southern sites. Extreme precipitation intensity was greater in mountainous areas, and precipitation frequency increased in the plain region. 5) Factor analysis revealed good correlations among extreme temperature indices, excepting extreme temperature days.

Extreme temperature and precipitation events in Alaska are examined in an ensemble of global climate models (GCMs) and an atmospheric reanalysis. Extreme monthly maximum and minimum temperature and the monthly maximum 5-day precipitation amount are evaluated for a 30-year historical period and two 30-year future time slices (2050s and 2080s). Although biases exist, models capture the spatial pattern and seasonality of the extremes depicted by the ERA-40 reanalysis. Discrepancies between station data (Anchorage, Fairbanks and Barrow) and GCMs/reanalysis are larger than the model-reanalysis differences, and are consistent with (1) surface elevation differences arising from the models' resolution and (2) gauge undercatch of precipitation in the station data. GCMs project future changes that are 2–4 times larger than the across-model standard deviations. The largest changes projected by the GCMs are significantly different from the historical mean at the 95% confidence level. Changes in extreme minimum temperature and extreme 5-day precipitation projections are larger than changes in means. The extreme minimum temperatures are projected to increase 2–3 times as much as the extreme maximum temperatures in all seasons except summer, with the largest increases of extreme minima in coastal regions. By the 2080s, the increases in all three extremes indices are twice as large in the Representative Concentration Pathway (RCP) 8.5 as in the RCP 4.5 scenario. The magnitude of the projected increase of maximum 5-day precipitation is largest in southern and inland areas, although the percentage increase is largest in the north. In the RCP 8.5 simulations, the inter-annual variability of extreme temperatures narrows by the end of the century, most notably in autumn. Record-breaking 5-day precipitation events become more common in the RCP 8.5 than in the RCP 4.5 scenario.

Exploring the characteristic of the extreme climatic events, especially future projection is considerably important in assessing the impacts of climatic change on hydrology and water resources system. We investigate the future patterns of climate extremes (2001–2099) in the Haihe River Basin (HRB) derived from Coupled General Circulation Model (CGCM) multimodel ensemble projections using the Bayesian Model Average (BMA) approach, under a range of emission scenarios. The extremes are depicted by three extreme temperature indices (i.e., frost days (FD), growing season length (GSL), and Tmin >90th percentile (TN90)) and five extreme precipitation indices (i.e., consecutive dry days (CDD), precipitation ≥10 mm (R10), maximum 5-day precipitation total (R5D), precipitation >95th percentile (R95T), and simple daily intensity index (SDII)). The results indicate frost days display negative trend over the HRB in the 21st century, particularly in the southern basin. Moreover, a greater season length and more frequent warm nights are also projected in the basin. The decreasing CDD, together with the increasing R10, R5D, R95T, and SDII in the 21st century indicate that the extreme precipitation events will increase in their intensity and frequency in the basin. Meanwhile, the changes of all eight extremes climate indices under A2 and A1B scenarios are more pronounced than in B1. The results will be of practical significance in mitigation of the detrimental effects of variations of climatic extremes and improve the regional strategy for water resource and eco-environment management, particularly for the HRB characterized by the severe water shortages and fragile ecological environment.

&amp;lt;p&amp;gt;The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models. Work is underway to determine whether these simulations provide a range of historical regional climate variability suitable for statistically increasing the observed temperature sample.&amp;amp;#160; Alternative sequences of historical temperature can be constructed by combining a forced signal with estimates of internal climate noise; prior studies have used the forced response from one SMILE in concert with observational noise resampling to form an &amp;amp;#8220;observational large ensemble&amp;amp;#8221; (McKinnon et al. 2018). Analogous to a SMILE, an observational large ensemble can be used to statistically contextualize monthly to half-yearly extreme events, such as the persistently mild Siberian winter of 2020, and to develop additional extended hot or cold spell storylines to explore in future projections of regional climate.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, an alternative approach to constructing an observational large ensemble of European surface air temperature over the historical period (1950-2014), made possible by the MMLEA, is explored. Rather than relying on forced response and internal variability, components not well-defined in the single realization of observed climate, the constructed circulation analogue method of dynamical adjustment is employed to separate temperature anomalies related to atmospheric circulation (&amp;amp;#8220;dynamic noise&amp;quot;) from a more thermodynamically driven residual signal. The approach is advantageous because it can be applied in a similar manner to single realizations from both models and observations. Here, dynamic noise is computed by dividing each of the seven CMIP5-generation SMILEs in half and empirically estimating the component of temperature associated with interannual sea level pressure variability in one half of the SMILE using circulation analogues from members in the other half. Because ensemble means can be computed in SMILEs, it is possible to use the relationship between unforced temperature and unforced sea level pressure anomalies to construct dynamic noise. In observations, weekly-averaged analogues are assessed as a means to increase the size of the analogue pool such that the separation between dynamic noise and thermodynamic residual signal occurs in a manner more similar to that computed in the SMILEs.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The extent to which dynamic noise fields from different SMILEs are distinguishable from each other and from observational estimates is determined via spectral and spatial pattern analyses. To avoid introducing regional model bias into dynamic noise estimates, a mosaic approach will be taken; noise estimates from different models are mosaiced such that observed statistical properties are maintained at each grid point of the European domain. Upon validation, SMILE-derived dynamic noise and observational thermodynamic residual signal estimates are combined into a 50-member European observational large ensemble and evaluated via a multi-month extreme temperature frequency metric against the observational large ensemble developed by McKinnon et al. (2018). Anomalously persistent hot and cold spells found in the European observational large ensemble are further compared to events in out-of-sample future projections of climate from the CMIP6 archive.&amp;lt;/p&amp;gt;

We present an analysis of a present-day climate simulation (1990–2009) for the Southern Africa region with the RegCM4 regional climate model (RCM, dx = 25 km ~0.22°) driven by the Community Atmospheric Model version 4 (CAM4) global climate model (GCM, dx = 1°). We assess the capability of the models to simulate the observed climate of the region with emphasis on precipitation, 2-m mean, minimum, and maximum temperature and large-scale circulation. The analysis focuses on seasonal climatologies, annual cycles, interannual variability, and extreme events. In addition to evaluating the performance of the models, we also attempt to assess the added value of the regional model compared to the driving one. With a few exceptions, we find that the models reproduce reasonably well the mean spatial patterns of 2-m temperature and precipitation, along with the associated seasonal cycle and interannual variability over selected sub-regions. Extreme indices of temperature and precipitation are also reasonably well reproduced. However, the RegCM4 substantially improves the simulation of daily precipitation frequency and duration of dry and wet events compared to CAM4 due to its higher resolution. The global and regional models exhibit quite different patterns of bias, an indication of the importance of internal variability and process representation for the simulation of surface climate. Given the good performance shown by the nested CAM4-RegCM4 system, we plan to use these models to generate an ensemble of projections for use in impact assessment studies for the region.

The Brazilian Midwest has significant spatiotemporal variability in terms of precipitation and air temperature, making it more vulnerable to the occurrence of extreme weather events. The objective of this study is to characterize the trend of extreme climatic events regarding precipitation and air temperature in the Brazilian Midwest, and to analyze their relationship with Pacific and Atlantic Sea Surface Temperature anomalies (SSTAs). We used daily precipitation and air temperature data measured at 24 conventional weather stations. Pacific and Atlantic SSTA data were obtained from the Climate Prediction Center. The frequency of hot extremes had increased, while that of cold extremes had decreased significantly, thus highlighting the consistent warming across the Brazilian Midwest. The precipitation extremes had greater variability than the temperature extremes. Precipitation intensity increased in Amazonia, with no change in annual precipitation volume. The precipitation extremes in the Brazilian Savanna, Pantanal, and the Atlantic Forest did not have a well-defined pattern but indicated a trend towards a decrease in days with intense precipitation events. In general, the Equatorial Pacific and Atlantic Ocean (TNAI and TSAI) SSTAs were negatively correlated with precipitation extreme indices and positively correlated with air temperature extreme indices in the Amazon. However, the North Atlantic SSTAs were positively correlated with precipitation and air temperature extreme indices in the Brazilian Savanna and Pantanal. In addition, the Pacific SSTAs were positively correlated with precipitation intensity in the Atlantic Forest. Thus, the variability of the trends of precipitation and air temperature extreme indices in the Brazilian Midwest was observed, and it was surmised that this measure was significantly related to Pacific and Atlantic SSTAs.

The Silk Road Economic Belt and 21st‐century Maritime Silk Road is a development initiative proposed by the Chinese government. However, the Belt and Road regions involve a large population, and regional economic development is sensitive to climate change, particularly climate extreme events. Hence, it is of vital relevance to pay more attention to the climate extreme change over this region. In this study, changes in the temperature‐ and precipitation‐related extremes over the Belt and Road regions are evaluated during the middle and the end of this century using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. Results present obvious changes in extreme temperature and precipitation indices under both RCP4.5 (representative concentration pathways) and RCP8.5 scenarios against the current climate state (1986–2005). Changes generally present relatively larger magnitudes under RCP8.5 than RCP4.5. Projected multi‐model ensemble results show a significant increase in warm events over Moscow and Nairobi. Additionally, cold events will decrease over central Europe, Moscow and central Asia, along with longer growing season length over these regions. The annual mean precipitation is reported to significantly increase over the region of Nairobi and the flooding events will be exacerbated across the Belt regions under a future warmer world, particularly over Moscow, Southeast Asia and Nairobi. Meanwhile, the region of West Asia will be likely to experience more drought and flooding events with the warming. However, we should also note that the inter‐model uncertainty of these results is reported to increase with time and a relatively large model spread can be seen in precipitation‐related indices when comparing with the temperature.

Glaciers are susceptible indicators of climate change and crucial parts of the world’s water cycle. In the context of global warming, we took the Urumqi Glacier No.1 (UG1) as an example, which is situated at the source of the Urumqi River on the northern slope of the Tianshan Mountains, Xinjiang, combined with the climate data of Daxigou Meteorological Station from 1980 to 2020, and the change of glacier mass balance and its response to extreme climate are discussed. The results suggest that the glacier mass balance of UG1 showed a downward trend over the studied 41-year period, and the mass loss increased. The cumulative glacier mass balance value was −19,776 mm w.e., and the average annual value was −482 mm w.e.a−1. The Mann-Kendall trend test showed that the change point occurred around 1994, and the mass balance of UG1 became more negative after 1994. In the same period, the changing mass balance trend of UG1 was not the same in different seasons. The inter-annual variation of summer mass balance was drastic, showing a marked downward trend; the inter-annual change of winter mass balance was small, showing a slight uptrend. The changing of extreme climate indices where UG1 is located showed that only TX90p and TX10p changed observably from 1980 to 2020, and the extreme precipitation indices changed evidently and had been on the rise. The changing trend of extreme climate indices indicated that the temperature was rising, the warming was significant, and the precipitation was increasing. During 1980–2020, the glacier mass balance was substantially correlated with the extreme temperature indices (TX90p, TXx) but not with the extreme precipitation indices. Analyzing on a seasonal scale, the summer mass balance was memorably correlated with the extreme temperature indices (TX90p, TX10p, TXx), and the correlation coefficient between winter mass balance and the extreme precipitation index R95p and winter precipitation was in the range 0.36~0.40 (p &lt; 0.05). According to the correlation between glacier mass balance and extreme climate indices, the summer mass balance was mainly affected by temperature, and the winter mass balance was affected primarily by precipitation.

Uncertainty quantification and characterization in changing climate scenarios have a direct impact on the efforts toward mitigation and adaptation. The chaotic and nonlinear nature of atmospheric processes results in high sensitivity to initial conditions resulting in considerable variability. Multiple model ensembles of Earth System Models are often used to visualize the role of parametric uncertainties in mean and extreme attributes of precipitation trends in various time horizons. However, studies quantifying the role of internal variability in controlling extreme precipitation statistics in decadal and interdecadal scales are limited. Specifically, we quantify the relative contribution of uncertainty due to internal variability and model uncertainty in the depth and volatility of Indian Summer Monsoon rainfall extremes of different duration and frequencies. We establish that the role of internal variability in extreme precipitation indices such as 100‐years and 30‐years return levels are comparable to the uncertainty arising from structural differences in the model captured through bias‐corrected ensembles of multimodel outputs. From the regional analysis, we find that internal variability is not only comparable, but it also shows higher mean and uncertainty in estimating extreme precipitation indices in central India. The intensifying precipitation extremes have quantifiable impacts on Depth Duration Frequency (DDF) curves, which directly implicate hydraulic design and water resources planning and management. We show that combining outputs from multiple initial condition ensembles generated to span the range of internal climate variability can help us reduce uncertainty and provide the distinguished uncertainty bounds of DDF curves.

The most extreme precipitation event in Metropolitan Lima (ML) occurred on 15 January 1970 (16 mm), this event caused serious damage, and the real vulnerability of this city was evidenced; the population is still not prepared to resist events of this nature. This research describes the local climate variability and extreme climate indices of temperature and precipitation. In addition, the most extreme precipitation event in ML is analyzed. Extreme climate indices were identified based on the methodology proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Some extreme temperature indices highlight an initial trend toward warm conditions (1965–1998); this trend has changed towards cold conditions since 1999, consistent with the thermal cooling during the last two decades in ML (−0.5 °C/decade) and other coastal areas of Peru. The variations of extreme temperature indices are mainly modulated by sea-surface temperature (SST) alterations in the Niño 1 + 2 region (moderate to strong correlations were found). Extreme precipitation indices show trends toward wet conditions after the 1980s, the influence of the Pacific Ocean SST on the extreme precipitation indices in ML is weak and variable in sign. The most extreme precipitation event in ML is associated with a convergence process between moisture fluxes from the east (Amazon region) at high and mid levels and moisture fluxes from the west (Pacific Ocean) at low levels, and near the surface.

Reliable projections of monsoon mean and extreme precipitation are vital to water resource management, disaster mitigation as well as policy makings. Since the climate models have better capability in projecting mean precipitation, the relationship between mean and extreme precipitation is a crucial clue for reliable projections of extreme precipitation. However, selections of optimal models in reproducing historical mean/extreme precipitation and their relationship were rarely reported. Here, more credible projections of mean/extreme precipitation and their connections over Northern Hemisphere land monsoon domain (NHLMD) were explored after a systematical assessment of the models from Coupled Model Intercomparsion Project Phase 6 (CMIP6) and CMIP5. Results indicated that most models could well reproduce the climatology and the variation of mean precipitation over NHLMD, while large portions have underestimated the extreme precipitation threshold (EPTH) and overestimated the extreme precipitation days (EPDs). However, nearly all models captured the observed robust relationships between mean and extreme precipitation. The projected mean precipitation and EPDs exhibit coherent variations with increase trend over Asian‐North African monsoon, and decrease trend over North American monsoon. The significant positive correlations between mean precipitation and EPDs will continue during 21st century. Considering the relatively low skills and large uncertainty in the simulation and the projection of EPDs, the tight connection could be used to reliably project the future extreme precipitation over the NHLMD using the projection of monsoon mean precipitation via the selected models.

The objective of the current study was to project changes in extreme temperature indices over Iran by the end of the 21st Century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. We used six extreme temperature indices recommended and defined by the Expert Team on Climate Change Detection and Indices from a set of 18 CMIP5 model simulations from different modelling centres. The extreme temperature indices simulation results of the models were compared with the gridded extreme temperature indices observations of the Asfazari National Database with a spatial resolution of 15 × 15 km during the period 1962–2005. For comparison purposes, the CMIP5 model data and the Asfazari data were regridded to a uniform horizontal resolution 0.5° × 0.5° grid using a bilinear interpolation scheme before further processing. After first validating the model performance by computing bias, future changes in the period 2006–2100 were projected. The modified Mann Kendall trend test and the Sen slope estimator test were used for evaluating the trend of indices and estimating the change rate, respectively, at a 95% confidence level. The results show that the minimum of the daily minimum temperature (TNn) and the minimum of the daily maximum temperature (TXn) will increase in the future. Warming of night time temperature indices (TNn and TN90p) are projected to be stronger than those of daytime temperature indices (TXn and TX90p). Warming is not uniform across the country, with considerably more warming in the western and southern parts. In terms of the frequency of frost days, a decrease is generally projected in the 21st Century. The largest decrease is projected for the Zagros mountain ranges in the west and the mountainous region in the northeastern part of the country with a magnitude of around 8–10 days⋅decade−1. The number of heat wave days is expected to increase sharply from about 13 days in the reference period (1963–2005) to 100 days by mid‐century to more than 235 days by the end of the century under Representative Concentration Pathway 8.5.