Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology

Abstract Global warming has been linked to systematic changes in North and South America's climates and may severely impact the North American monsoon system (NAMS) and South American monsoon system (SAMS). This study examines interannual-to-decadal variations and changes in the low-troposphere (850 hPa) temperature (T850) and specific humidity (Q850) and relationships with daily precipitation over the tropical Americas using the NCEP–NCAR reanalysis, the Climate Forecast System Reanalysis (CFSR), and phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations for two scenarios: “historic” and high-emission representative concentration pathway 8.5 (RCP8.5). Trends in the magnitude and area of the 85th percentiles were distinctly examined over North America (NA) and South America (SA) during the peak of the respective monsoon season. The historic simulations (1951–2005) and the two reanalyses agree well and indicate that significant warming has occurred over tropical SA with a remarkable increase in the area and magnitude of the 85th percentile in the last decade (1996–2005). The RCP8.5 CMIP5 ensemble mean projects an increase in the T850 85th percentile of about 2.5°C (2.8°C) by 2050 and 4.8°C (5.5°C) over SA (NA) by 2095 relative to 1955. The area of SA (NA) with T850 ≥ the 85th percentile is projected to increase from ~10% (15%) in 1955 to ~58% (~33%) by 2050 and ~80% (~50%) by 2095. The respective increase in the 85th percentile of Q850 is about 3 g kg−1 over SAMS and NAMS by 2095. CMIP5 models project variable changes in daily precipitation over the tropical Americas. The most consistent is increased rainfall in the intertropical convergence zone in December–February (DJF) and June–August (JJA) and decreased precipitation over NAMS in JJA.

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Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

10.5194/egusphere-egu21-9722 ◽

2021 ◽

Author(s):

Thordis Thorarinsdottir ◽

Jana Sillmann ◽

Marion Haugen ◽

Nadine Gissibl ◽

Marit Sandstad

Keyword(s):

Air Temperature ◽

Model Evaluation ◽

Climate Models ◽

Climate Model ◽

Mean Squared Error ◽

Surface Air Temperature ◽

Cmip5 Models ◽

Model Output ◽

Intergovernmental Panel ◽

Near Surface

Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important&#160;as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.

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Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

Journal of Geophysical Research Atmospheres ◽

10.1029/2009jd012556 ◽

2010 ◽

Vol 115 (D12) ◽

Cited By ~ 28

Author(s):

Marlis Hofer ◽

Thomas Mölg ◽

Ben Marzeion ◽

Georg Kaser

Keyword(s):

High Resolution ◽

Air Temperature ◽

Statistical Downscaling ◽

Reanalysis Data ◽

Specific Humidity ◽

Glacier Surface ◽

Cordillera Blanca

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Earth system responses to cumulative carbon emissions

Biogeosciences Discussions ◽

10.5194/bgd-12-9839-2015 ◽

2015 ◽

Vol 12 (13) ◽

pp. 9839-9877 ◽

Cited By ~ 3

Author(s):

M. Steinacher ◽

F. Joos

Keyword(s):

Air Temperature ◽

Carbon Emissions ◽

Climate Model ◽

Climate Adaptation ◽

Water Saturation ◽

Cmip5 Models ◽

Large Set ◽

Steric Sea Level ◽

Transient Climate Response ◽

Biogenic Structures

Abstract. Information on the relationship between cumulative fossil carbon emissions and multiple climate targets are essential to design emission mitigation and climate adaptation strategies. In this study, the transient responses in different climate variables are quantified for a large set of multi-forcing scenarios extended to year 2300 towards stabilization and in idealized experiments using the Bern3D-LPJ carbon-climate model. The model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte-Carlo type framework. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.88 °C (68 % confidence interval (c.i.): 1.28 to 2.69 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and in steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic Meridional Overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The slopes of the relationships change when CO2 is stabilized. The Transient Climate Response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the Equilibrium Climate Sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models, but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

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Correlation and Causation Analysis Between COVID-19 and Environmental Factors in China

Frontiers in Climate ◽

10.3389/fclim.2021.619338 ◽

2021 ◽

Vol 3 ◽

Author(s):

Zuofang Zheng ◽

Junxia Dou ◽

Conglan Cheng ◽

Hua Gao

Keyword(s):

Wind Speed ◽

Air Temperature ◽

Regional Climate ◽

Climate Impact ◽

Control Measures ◽

Specific Humidity ◽

Human Society ◽

Causality Test ◽

Low Wind Speed ◽

Causation Analysis

Coronavirus disease 2019 (COVID-19) is seriously threatening and altering human society. Although prevention and control measures play an important role in preventing the transmission of severe acute respiratory syndrome coronavirus, signals of climate impact can still be detected globally. In this paper, the data of 265 cities in China were analyzed. The results show that the correlations between COVID-19 and air quality index (AQI) and PM2.5 concentration were very weak and that the correlations between COVID-19 and meteorological factors were significantly different in different climate backgrounds. So, a fixed model is not enough to describe the correlations. Overall, high humidity, low wind speed, and relatively lower air temperature are conducive to the spread of COVID-19. The climate background suitable for the spread of COVID-19 in China is air temperature 0~15°C, specific humidity <3 g kg−1, and wind speed <3 m s−1. The Granger causality test shows that there is a causal relationship between daily average air temperature and the number of COVID-19 confirmed cases in some cities of China, and air temperature is indicative of the number of confirmed cases the next day. However, this phenomenon is not universal due to regional climate differences.

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