scholarly journals Changes in annual extremes of daily temperature and precipitation in CMIP6 models

2020 ◽  
pp. 1-61
Author(s):  
Chao Li ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Guilong Li ◽  
Ying Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Daily Temperature ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Hot Temperature ◽  
Spatial Pooling ◽  
Temperature And Precipitation ◽  
New Generation

Abstract:This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most land masses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius-Clapeyron rate of ∼7%/°C. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

scholarly journals Estimating changes of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution

2018 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Tyler C. Sutterley ◽  
Yara Mohajerani ◽  
Enrico Ciraci ◽  
...  
Keyword(s):  
Extreme Temperature ◽  
Extreme Value ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Return Periods ◽  
Generalized Extreme Value ◽  
Gev Distribution ◽  
Cold Extreme ◽  
Temperature And Precipitation

Abstract. Changes in extreme temperature and precipitation may give some of the largest significant societal and ecological impacts. For changes in the magnitude of extreme temperature and precipitation over India, we used a statistical model of generalized extreme value (GEV) distribution. The GEV statistical distribution is a time-dependent distribution with different time scales of variability bounded by a precipitation, maximum (Tmax), and minimum (Tmin) temperature extremes and also assessed their possibility changes are evaluated and quantified over India is presented. The GEV-based method is applied on both precipitation and temperature extremes over India during the 20th and 21st centuries using multiple coupled climate models taking an interest in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and observational datasets. The regional means of historical warm extreme temperatures are 34.89, 36.42, and 38.14 °C for three different (10, 20, and 50-year) periods, respectively; whereas the cold extreme mean temperatures are 7.75, 4.19, and −1.57 °C. It indicates that 20th century cold extreme temperatures have relatively larger variations than the warm extremes. As for the future, the CMIP5 models of warm extreme regional mean values increase from 0.33 to 0.75 °C in all return periods (10-, 20-, and 50-year periods), while in the case of cold extreme means values vary between 0.58 and 2.29 °C. In the future, cold extreme values have a larger increasing rate over the northwest, northeast, some parts of north-central, and Inter Peninsula regions. The CRU precipitation extremes are larger than the historical extreme precipitation in all three (10, 20, and 50-year) return-periods.

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals Climate regionalization and trends based on daily temperature and precipitation extremes in the south of the Pampas (Argentina)

2019 ◽  
Vol 45 (1) ◽  
pp. 393 ◽  
Author(s):  
F. Ferrelli ◽  
A.S. Brendel ◽  
V.S. Aliaga ◽  
M.C. Piccolo ◽  
G.M.E. Perillo
Keyword(s):  
Daily Temperature ◽  
Precipitation Extremes ◽  
Maximum Temperature ◽  
Semiarid Region ◽  
The South ◽  
Short Term ◽  
Substantial Impact ◽  
Temperature And Precipitation ◽  
High Dependency ◽  
Climate Regionalization

The south of Pampas (36° 32’-40° 44’ S; 63° 24’-60° 30’ W), as most of Argentina, is a semiarid region. Its economy is based on rain-fed agriculture and livestock. Traditionally, the climate has been studied considering the analyses of monthly and annual climate parameters, but there is evidence that in this type of areas, the short-term climatic events have a substantial impact on the climate. Therefore, this study aimed at developing a climate regionalization from the analysis of daily temperature and precipitation extremes in the south of the Pampas for the period 1970-2017. Subsequently, it focuses on analyzing both trends and breakpoints of these events in the different sub-climates. To do so, we applied a Cluster-based Principal Component Analyses with a Ward hierarchical supervised method to generate a climate regionalization considering 29 daily extreme climatic indices and the elevation. We identify four sub-regions, and we analyzed trends during 1970-2017, and in the two-time series defined by applying breakpoints. Both minimum and maximum temperatures and precipitation had structural changes in the last 15 years, exposing the region to warming and dryness trends. The maximum temperature increases 0.5ºC, while precipitation decreases 30 mm. The short-term climate variability allows us to identify areas climatically more detailed and to conclude that the south of the Pampas is characterized by its high dependency on short-term climatic events.

scholarly journals Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

2015 ◽  
Vol 28 (6) ◽  
pp. 2332-2348 ◽  
Author(s):  
G. Abramowitz ◽  
C. H. Bishop
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Annual Rainfall ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Twenty First Century

Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

scholarly journals Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps

2017 ◽  
Vol 30 (8) ◽  
pp. 2829-2847 ◽  
Author(s):  
Paul C. Loikith ◽  
Benjamin R. Lintner ◽  
Alex Sweeney
Keyword(s):  
United States ◽  
Large Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Local Scale ◽  
Precipitation Extremes ◽  
Self Organizing Maps ◽  
Temperature And Precipitation ◽  
Self Organizing

The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales.

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