scholarly journals Evaluation of the Performance of CMIP5 Models to Simulate Land Surface Air Temperature Based on Long-Range Correlation

2021 ◽  
Vol 9 ◽  
Author(s):  
Shanshan Zhao ◽  
Wenping He ◽  
Tianyun Dong ◽  
Jie Zhou ◽  
Xiaoqiang Xie ◽  
...  
Keyword(s):  
Long Range ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Fluctuation Analysis ◽  
Distribution Characteristics ◽  
Scaling Exponents ◽  
Zonal Distribution ◽  
Range Correlation

The daily average land surface air temperature (SAT) simulated by 8 CMIP5 models historical experiments and that from NCEP data during 1960–2005, are used to evaluate the performance of the CMIP5 model based on detrended fluctuation analysis (DFA) method. The DFA results of NCEP data show that SAT in most regions of the world exhibit long-range correlation. The scaling exponents of NCEP SAT show the zonal distribution characteristics of larg in tropics while small in medium and high latitudes. The distribution characteristics of the zonal average scaling exponents of CMCC-CMS, GFDL-ESM2G, IPSL-CM5A-MR are similar to that of NCEP data. From the DFA errors of model-simulated SAT, the performance of IPSL-CM5A-MR is the best among the 8 models throughout the year, the performance of FGOALS-g2 is good in spring and summer, GFDL-ESM2G is the best in autumn, CNRM-CM5 and CMCC-CMS is good in winter. The scaling exponents of model-simulated SAT are closer to that of NCEP data in most areas of the mid-high latitude on the northern hemisphere. However, simulations of SAT in East Asia and Central North American are generally less effective. In spring, most models have better performance in Siberian (SIB), Central Asia (CAS) and Tibetan (TIB). SAT in Northern Europe area are well simulated by most models in summer. In autumn, areas with better performance of most models are Mediterranean, SIB and TIB regions. In winter, SAT in Greenland, SIB and TIB areas are well simulated by most models. Generally speaking, the performance of CMIP5 models for SAT on global continents varies in different seasons and different regions.

scholarly journals On the scaling effect in global surface air temperature anomalies

2013 ◽  
Vol 13 (10) ◽  
pp. 5243-5253 ◽  
Author(s):  
C. A. Varotsos ◽  
M. N. Efstathiou ◽  
A. P. Cracknell
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Sea Surface ◽  
Scaling Exponents ◽  
Mean Values ◽  
Temperature Anomalies ◽  
Air Temperature Anomalies

Abstract. The annual and the monthly mean values of the land-surface air temperature anomalies from 1880–2011, over both hemispheres, are used to investigate the existence of long-range correlations in their temporal evolution. The analytical tool employed is the detrended fluctuation analysis, which eliminates the noise of the non-stationarities that characterize the land-surface air temperature anomalies in both hemispheres. The reliability of the results obtained from this tool (e.g., power-law scaling) is investigated, especially for large scales, by using error bounds statistics, the autocorrelation function (e.g., rejection of its exponential decay) and the method of local slopes (e.g., their constancy in a sufficient range). The main finding is that deviations of one sign of the land-surface air temperature anomalies in both hemispheres are generally followed by deviations with the same sign at different time intervals. In other words, the land-surface air temperature anomalies exhibit persistent behaviour, i.e., deviations tend to keep the same sign. Taking into account our earlier study, according to which the land and sea surface temperature anomalies exhibit scaling behaviour in the Northern and Southern Hemisphere, we conclude that the difference between the scaling exponents mainly stems from the sea surface temperature, which exhibits a stronger memory in the Southern than in the Northern Hemisphere. Moreover, the variability of the scaling exponents of the annual mean values of the land-surface air temperature anomalies versus latitude shows an increasing trend from the low latitudes to polar regions, starting from the classical random walk (white noise) over the tropics. There is a gradual increase of the scaling exponent from low to high latitudes (which is stronger over the Southern Hemisphere).

Quantifying Fractal Dynamics of Metallogenic Systems with Detrended Fluctuation Analysis

2012 ◽  
Vol 249-250 ◽  
pp. 26-30 ◽  
Author(s):  
Li Wan ◽  
Peng Chen ◽  
Zhao Xian Gong
Keyword(s):  
Long Range ◽  
Scale Invariance ◽  
Detrended Fluctuation Analysis ◽  
Dynamic Mechanism ◽  
Fluctuation Analysis ◽  
Fractional Dynamics ◽  
Scaling Exponents ◽  
Range Correlation ◽  
Detrended Fluctuation

In this paper, we analysed fractional dynamics behavior in metallogenic elements grade series, using detrended fluctuation analysis (DFA), with the objective to explore and understand the underlying dynamic mechanism. Our results show that the metallogenic elements grade series are the scale invariance and the long-range correlation. As in the case of element grade dynamics, the DFA scaling exponents can be used to discriminate mineral intensity.

scholarly journals On the scaling effect in global surface air temperature anomalies

2012 ◽  
Vol 12 (6) ◽  
pp. 14727-14746
Author(s):  
C. A. Varotsos ◽  
M. N. Efstathiou
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Sea Surface ◽  
Scaling Exponents ◽  
Mean Values ◽  
Temperature Anomalies ◽  
Air Temperature Anomalies

Abstract. The annual and the monthly mean values of the land-surface air temperature anomalies during 1880–2011, over both hemispheres, are used to investigate the existence of long-range correlations in their temporal march. The analytical tool employed is the detrended fluctuation analysis which eliminates the noise of the non-stationarities that characterize the land-surface air temperature anomalies in both hemispheres. The main result obtained is that deviations of one sign of the land-surface air temperature anomalies in both hemispheres are generally followed by deviations with the same sign at different time intervals. In other words the land-surface air temperature anomalies exhibit persistent behaviour i.e., deviations tend to keep the same sign. Specifically, the scaling exponents of the annual (monthly) mean land-surface air temperature anomalies, α = 0.65 (0.73–0.75), are roughly equal in both hemispheres approaching to that of the global annual (monthly) mean land-surface air temperature anomalies, α =0.68 (0.80). Taking into account our earlier study according to which the land and sea surface temperature anomalies obey scaling exponents α =0.78 and α = 0.89 in the Northern and Southern Hemisphere, respectively, we conclude that the difference between the scaling exponents in both sea and land contributions to the surface air temperature stems mainly from the sea surface temperature, which exhibits stronger memory in the Southern than in the Northern Hemisphere. This conclusion may be attributed to the fact that oceans have the greatest capacity to store heat, being thus able to regulate the temperature on land with less pronounced persistence. Moreover, the variability of the scaling-exponents of the annual mean values of the land-surface air temperature anomalies versus latitude shows an increasing trend from the low to polar regions starting from the classical random walk (white noise) over tropics. The gradual increase of the scaling exponent from the low to high latitudes (which is stronger over the Southern Hemisphere) could be associated with the poleward increase in climate sensitivity predicted by the global climate models. In this context, the persistence in the land-surface air temperature enhances the feasibility of its reliable long-term forecast, which is very important for various climate applications.

scholarly journals Whether the CMIP5 Models Can Reproduce the Long-Range Correlation of Daily Precipitation?

2021 ◽  
Vol 9 ◽  
Author(s):  
Tianyun Dong ◽  
Shanshan Zhao ◽  
Ying Mei ◽  
Xiaoqiang Xie ◽  
Shiquan Wan ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Long Range ◽  
Daily Precipitation ◽  
Cmip5 Models ◽  
High Latitudes ◽  
Scaling Exponents ◽  
Absolute Value ◽  
Four Seasons ◽  
Range Correlation ◽  
The Tropics

In this study, we investigated the performance of nine CMIP5 models for global daily precipitation by comparing with NCEP data from 1960 to 2005 based on the detrended fluctuation analysis (DFA) method. We found that NCEP daily precipitation exhibits long-range correlation (LRC) characteristics in most regions of the world. The LRC of daily precipitation over the central of North American continent is the strongest in summer, while the LRC of precipitation is the weakest for the equatorial central Pacific Ocean. The zonal average scaling exponents of NCEP daily precipitation are smaller in middle and high latitudes than those in the tropics. The scaling exponents are above 0.9 over the tropical middle and east Pacific Ocean for the year and four seasons. Most CMIP5 models can capture the characteristic that zonal mean scaling exponents of daily precipitation reach the peak in the tropics, and then decrease rapidly with the latitude increasing. The zonal mean scaling exponents simulated by CMCC-CMS, GFDL-ESM2G and IPSL-CM5A-MR show consistencies with those of NCEP, while BCC_CSM1.1(m) and FGOALS-g2 cannot capture the seasonal variations of daily precipitation’s LRC. The biases of scaling exponents between CMIP5 models and NCEP are smaller in the high latitudes, and even less than the absolute value of 0.05 in some regions, including Arctic Ocean, Siberian, Southern Ocean and Antarctic. However, for Western Africa, Eastern Africa, Tropical Eastern Pacific and Northern South America, the simulated biases of scaling exponents are greater than the absolute value of 0.05 for the year and all four seasons. In general, the spatial biases of LRC simulated by GFDL-ESM2G, HadGEM2-AO and INM-CM4 are relatively small, which indicating that the LRC characteristics of daily precipitation are well simulated by these models.

scholarly journals Comparisons of Time Series of Annual Mean Surface Air Temperature for China since the 1900s: Observations, Model Simulations, and Extended Reanalysis

2017 ◽  
Vol 98 (4) ◽  
pp. 699-711 ◽  
Author(s):  
Qingxiang Li ◽  
Lei Zhang ◽  
Wenhui Xu ◽  
Tianjun Zhou ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Land Surface ◽  
Linear Trend ◽  
Surface Air Temperature ◽  
Climate Simulation ◽  
Cmip5 Models ◽  
Near Surface ◽  
Uncertainty Range ◽  
Homogeneity Assessment

Abstract Time series of global or regional average surface air temperature (SAT) are fundamental to climate change studies. A number of studies have developed several national and regional SAT series for China, but because of the diversity of the meteorological observational sites, the different quality control routines for processing the data, and the inconsistency of the statistical methods used, they differ in their long-term trends. This paper assesses the similarities and differences of the existing time series of the annual average SAT for China that are based upon historical meteorological observations since the 1900s. The results indicate that the China average is similar to the series for the Northern Hemisphere (NH) landmass, except that the initial warming of the NH series derived from the CRUTEM3/4 datasets, which represent global historical land surface air temperatures and near-surface air temperature anomalies over land, respectively, ends earlier (before the early 1940s) than in China’s series. A major difference among the existing China average time series is the 1940s warmth, a period when there were very few observations across the country because of World War II. The SAT anomalies for China during the 1930s to 1940s have been reduced by improved homogeneity assessment compared to previous estimates. The new improved time series is in better agreement with both the historical twentieth-century reanalysis data and the historical climate simulation of phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. The new time series also shows the slowdown of the warming trend during the past 18 yr (1998–2015). The best estimate of a linear trend for increases in temperature with a 95% uncertainty range is 0.121° ± 0.009°C decade–1 for 1900–2015, indicating that the improved homogeneity assessment for China leads to a slightly greater trend than that based on raw data (0.107° ± 0.009°C decade–1).

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

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

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important 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.</p><p>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.</p><p>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.</p><p>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.</p>

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