Editing IPCC:AR6/WGI/Chapter-Atlas (section)

==== [[#Atlas.5.2.3|Atlas.5.2.3]] Assessment of Model Performance ====

<div id="h3-21-siblings" class="h3-siblings"></div>

Temperature trends and means derived from reanalysis datasets (JRA-25 and MERRA) correctly represented temperature variability shown in observational data over the Asian territory of Russia for the 1976–2010 period ( [[#Loginov--2014|Loginov et al., 2014]] ). Assessment of CRU TS 3.22, CRUTEMP4, ERA-Interim and NCEP2 datasets against station data over North Asia for annual and seasonal air temperature has shown that the ERA-Interim reanalysis outperforms others for the 1981–2005 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ). The latter reanalysis also underestimates summer precipitation and shows large wet biases over north-east Asia during spring and underestimates mean seasonal temperature over north-east Asia in spring (MAM), autumn (SON), and winter (DJF), but overestimates it in summer (JJA) compared with the CRU dataset ( ''medium confidence'' ) ( [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Top--2021|Top et al., 2021]] ).

GCMs capture the main synoptic processes affecting North Asia and the CMIP5 ensemble simulates the temporal evolution of the magnitude and position of the Siberian High (SH) over the period 1872–2005 ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). CMIP5 models simulate a weakened intensity of the winter SH and a strengthened interannual variability compared to observations ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). The characteristics of blocking events over the region (number, duration, intensity and frequency) were reasonably well reproduced by GCMs ( [[#Mokhov--2014|Mokhov et al., 2014]] ), and most overestimate the annual mean temperature over northern Eurasia (Interactive Atlas). Biases in simulated annual surface air temperature simulation primarily come from the winter (DJF) season and are relatively smaller in other seasons ( [[#Miao--2014|Miao et al., 2014]] ; [[#Peng--2019|Peng et al., 2019]] ). Most GCMs capture the main decadal SAT trend ( [[#Miao--2014|Miao et al., 2014]] ), though CMIP5 GCMs fail to capture the decreasing temperature trend over East Siberia ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). Possible causes of GCMs’ inability to represent the recent slowdown of warming is further discussed in Cross-Chapter Box 3.1. For CMIP5, models with higher resolution do not always perform better than those with lower resolutions ( ''medium confidence'' ) ( [[#Miao--2014|Miao et al., 2014]] ).

Sixteen CMIP5 model simulations of SAT variability over Eurasia were evaluated against CRU observations for permafrost sub-regions ( [[#Peng--2019|Peng et al., 2019]] ), showing a warm bias in north-west Eurasia, capturing the climate warming over the 20th century and its acceleration during the late 20th century. CMIP5 GCMs generally underestimate daily temperature range compared with observations over north-eastern Russia ( [[#Sillmann--2013|Sillmann et al., 2013]] ; [[#Lindvall--2015|Lindvall and Svensson, 2015]] ). Currently there is no literature on the CMIP6 ensemble over the region though a few single-model studies are available ( [[#Voldoire--2019|Voldoire et al., 2019]] ; T. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ).

There is very limited use of RCMs for North Asia. CORDEX-CAS covers North Asia, except parts of RFE, and ARCTIC-CORDEX covers the northern regions (Figure Atlas.6). For CORDEX-CAS three RCMs (REMO, ALARO-0 and CLMcom) have been used and have warm biases for maximum temperatures, cold biases for minimum temperatures and a wet bias in the north during the winter ( [[#Top--2021|Top et al., 2021]] ). Rain gauges, however, are known to have problems in terms of measuring properly solid precipitation (e.g., due to drifting snow) which can greatly affect the accuracy of precipitation observations over North Asia ( [[#Harris--2014|Harris et al., 2014]] ).

<div id="Atlas.5.2.4" class="h3-container"></div>

<span id="atlas.5.2.4-assessment-and-synthesis-of-projections"></span>