This study investigates observed and modeled contributions of global sea surface temperature (SST) to China winter climate trends in 1960-2014, including increased precipitation, warming through about 1997, and cooling since then. Observations and Atmospheric Model Intercomparison Project (AMIP) simulations with prescribed historical SST and sea ice show that tropical Indian Ocean (TIO) warming and increasing rainfall causes diabatic heating that generates a tropospheric wave train with anticyclonic 500-hPa height anomaly centers in the TIO or equatorial western Pacific (TIWP) and northeastern Eurasia (EA) and a cyclonic anomaly over China, referred to as the TIWP-EA wave train. The cyclonic anomaly causes Indochina moisture convergence and southwesterly moist flow that enhances South China precipitation, while the northern anticyclone enhances cold surges, sometimes causing severe ice storms. AMIP simulations show a 1960-1997 China cooling trend by simulating increasing instead of decreasing Arctic 500-hPa heights that move the northern anticyclone into Siberia, but enlarge the cyclonic anomaly so it still simulates realistic China precipitation trend patterns. A separate idealized TIO SST warming simulation simulates the TIWP-EA feature more realistically with correct precipitation patterns and supports the TIWP-EA teleconnection as the primary mechanism for long-term increasing precipitation in South China since 1960. Coupled Model Intercomparison Project (CMIP) experiments simulate a reduced TIO SST warming trend and weak precipitation trends, so the TIWP-EA feature is absent and strong drying is simulated in South China for 1960-1997. These simulations highlight the need for accurately modeled SST to correctly attribute regional climate trends.
Evaluating the capability of global climate models (GCMs) in reproducing the historical records represents a way of building confidence in their capability for future projections. Among the different ways of evaluating them, here we focus on their capability in reproducing the temporal clustering of heavy precipitation events across Europe in light of four climate modes [the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND)], and GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the large ensemble runs using the Community Earth System Model (CESM). We use a peak over threshold (POT) approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to the climate modes. We find that the GCMs can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; moreover, our findings indicate the GCMs can better reproduce the relationship between heavy precipitation and AO/SCAND, than NAO/EA. Comparing the results based on CMIP5 models and CESM, we find that the inter-model uncertainties are larger than the intra-model ones in most of the cases, even though CESM tends to have a poorer performance for EA; this shortcoming for CESM is likely due to the pattern of the Z500 anomalies which is different from the reference data when EA is in the positive phase, affecting the transport of moisture across Europe.
<p>Heavy precipitation has increased across many areas of the world, not only in terms of amounts but also of intensity and frequency, causing billions of dollars in economic losses and numerous fatalities. Our ability to prepare for and adapt to these events is tied to our understanding of the physical processes responsible for these events, and how they may respond to changes in anthropogenic forcings. Here we focus on the temporal clustering of heavy precipitation across Europe, highlight what the major climate drivers responsible for it are, and how it may change in response to changes in the concentration of greenhouse gasses. More specifically, we use a peak over threshold approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to four climate modes that have been connected with the occurrence of heavy precipitation across Europe: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND). We use outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), and experiments that allow us to focus on the response to CO<sub>2</sub> (pre-industrial, 1pctCO<sub>2</sub>, abrupt4&#215;CO<sub>2</sub>). To further detect the effects of downscaling on model-simulated precipitation, we also considered the accuracy of the EURO-CORDEX regional climate model (RCM) on capturing the temporal clustering in heavy precipitation across Europe. We find that: 1) the CMIP5 models can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; 2) the increases in CO<sub>2</sub> are expected to lead to a strengthening of the relationship between the climate modes and the occurrence of heavy precipitation events; 3) the response to an abrupt increase in CO<sub>2</sub> is generally stronger compared to a more gradual one.</p>
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