Xiaodong Yan

Spatiotemporal change in geographical distribution of global climate types in the context of climate warming

After standardizing global land climate gridded data from the Climatic Research Unit TS (time-series) 3.1 dataset for the period 1901–2009, cluster analysis is used to objectively classify world climates into 14 climate types. These climate types establish a baseline classification map and the types are named according to Köppen–Geiger climate classifications. Although the cluster analysis and Köppen classification methods are very different, the distributions of climate types obtained by the two methods are similar. Moreover, the climate types we identify also coincide well with their corresponding vegetation types. Thus, cluster analysis can be used as an effective alternative to the Köppen classification method for classifying world climate types. The spatial and temporal changes in geographical distribution of global climate types were investigated in 25-year intervals, and Cohen’s kappa coefficient is used to detect agreement between the periods. Globally, although an obvious trend in increasing global temperature is found, distribution of climate types overall show no distinct changes over the periods. However, at the regional scale, spatial change in distribution of climate types is evident in South America and Africa. In South America, larger areas of the “fully humid equatorial rainforest” (Af) and “equatorial savannah with dry winter” (Aw) climate types have changed types. In Africa, changes mainly occurred in the Af, “equatorial savannah with dry summer” (As), Aw, “steppe climate” (BS), and “desert climate” (BW) climate types. Moreover, some climate types, including Af, “equatorial monsoon” (Am), BS, BW, and “tundra climate” (ET), were susceptible to temporal climate changes, especially in the period 1976–2009.

A new statistical precipitation downscaling method with Bayesian model averaging: a case study in China

A new statistical downscaling method was developed and applied to downscale monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical downscaling: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better downscaling results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical downscaling. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical downscaling. In conclusion, the downscaling method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically downscale large-scale variables.

Using multi-model ensembles to improve the simulated effects of land use/cover change on temperature: a case study over northeast China

AbstractnRather than simulating the effects of land use and land cover change (LUCC) on the climate using one climate model, as in many previous studies, three regional climate models (Regional Climate Model, version 3; the Weather Research and Forecasting model; and the Regional Integrated Environmental Model System) were used in the present study to simulate changes in temperature due to LUCC. Two experiments (CTL and NE) were designed and run using the three regional climate models. The CTL experiment was used to compare the simulations of the different models and served to illustrate the improvement that could be achieved as a result of employing a multi-model ensemble. The NE experiment was used to evaluate the changes in temperature caused by LUCC in northeast China between 1981 and 2000. The results of the CTL simulations showed that changes in temperature were simulated well by the three regional climate models; however, the simulated temperatures were different, dependent on the model used. The multi-model ensembles [the arithmetic ensemble mean (AEM) and Bayesian model averaging (BMA)] attained better results than any individual model. Of the two ensemble methods, BMA performed better than the AEM. The effects of LUCC on the climate in northeast China were assessed by the differences between the CTL and NE simulations for every RCM and the ensemble simulations. The BMA simulations produced more reasonable results than the other simulations. Based on the results, we can state with some confidence that LUCC in northeast China over the 20-year period studied caused a decrease in temperature, because of an expansion of arable land.

Weight–length relationships and Fulton’s condition factors of skipjack tuna (Katsuwonus pelamis) in the western and central Pacific Ocean

This paper describes the weight–length relationships (WLRs) and Fulton’s condition factors (K) of skipjack tuna (Katsuwonus pelamis) in purse seine fisheries from three cruises in the western and central Pacific Ocean (WCPO): August–September 2009 (AS09), November–December 2012 (ND12), and June–July 2013 (JJ13). The fork length and weight of a total of 1678 specimens were measured. The results showed that the fork length of more than 70% of specimens was below 60 cm (76% in AS09, 87% in ND12, and 73% in JJ13). The coefficient b in the combined sex group was 3.367, 3.300 and 3.234 in JJ13, AS09 and ND12, respectively. The b values of WLRs when fork length was >60 cm were significantly less than 3 (P = 0.062), but when fork length was <60 cm they were significantly greater than 3 (P = 0.028). The K value ranges of JJ13, AS09 and ND12 in different fork length groups were 1.3–1.84 (1.62 ± 0.18), 1.57–2.02 (1.86 ± 0.15), and 1.44–1.78 (0.65 ± 0.13), respectively. Moreover, K values in different fork length classes for each cruise had one turning point: 60–65 cm for JJ13; 60–65 cm for ND12; and 55–60 cm for AS09. The results of this study provide basic information on the WLRs and K values of skipjack tuna in different seasons and growth phases in the WCPO, which are useful for fishery biologists and fishery managers.

Deficiencies in the simulation of the geographic distribution of climate types by global climate models

AbstractThe performances of General Circulation Models (GCMs) when checked with conventional methods (i.e. correlation, bias, root-mean-square error) can only be evaluated for each variable individually. The geographic distribution of climate type in GCM simulations, which reflects the spatial attributes of models and is related closely to the terrestrial biosphere, has not yet been evaluated.n Thus, whether the geographic distribution of climate types was well simulated by GCMs was evaluated in this study for nine GCMs. The results showed that large areas of climate zones classified by the GCMs were allocated incorrectly when compared to the basic climate zones established by observed data. The percentages of wrong areas covered approximately 30–50xa0% of the total land area for most models. In addition, the temporal shift in the distribution of climate zones according to the GCMs was found to be inaccurate. Not only were the locations of shifts poorly simulated, but also the areas of shift in climate zones. Overall, the geographic distribution of climate types was not simulated well by the GCMs, nor was the temporal shift in the distribution of climate zones. Thus, a new method on how to evaluate the simulated distribution of climate types for GCMs was provided in this study.n

Modeling precipitation changes in the Heihe River Basin, Northwest China, from 1980 to 2014 with the Regional Integrated Environment Modeling System (RIEMS) nested with ERA-Interim reanalysis data

This study presents the precipitation changes of the Heihe River Basin (HRB), Northwest China, using a long-term (1980–2014) and highly resolved modeling (up to 3xa0km by 3xa0km) with the Regional Integrated Environment Modeling System (RIEMS) forced by ERA-Interim reanalysis data. Firstly, the added value of high-resolution RIEMS simulation nested with ERA-Interim reanalysis was presented by comparisons among the RIEMS simulation results, site-based ground measurements, satellite-retrieved precipitation from TRMM 3B43 (V7), and the EAR-interim reanalysis data. The bias of the ERA-Interim reanalysis was largely corrected by the RIEMS simulation, although a little bias existed in the RIEMS simulation. RIEMS simulation showed that there was an increasing trend in the precipitation of the HRB from 1980 to 2014 with a rate of increase of approximately 3%/year for summer and 5%/year for autumn in the southeast part of the HRB. The increased precipitation mainly resulted from more precipitation days and the strengthening of daily precipitation. Upward motion from strengthened convergence over the eastern part of the HRB was the common reason for increased precipitation in summer and autumn, which was partly offset by decreased water vapor supply in summer while it was strengthened by increased water vapor supply in autumn. This paper presents precipitation changes for the HRB associated with global warming since 1980 and provides a higher-resolution climatological data set useful for reference climate impact studies.

Variations in droughts and wet spells and their influences in China: 1924–2013

The Wetness Index was calculated using gridded monthly precipitation and potential evapotranspiration in China during 1924–2013. Variations of dry and wet periods in different areas and the impact of meteorological factors were analyzed. The results show the following: (1) Drought areas (Wu2009≤u20090.5) and wet areas (Wu2009>u20090.65) in China constituted 33–56% and 35–55%, respectively, of the total land area and there were no secular trends during 1924–2013. During this period, the areas of drought and wet were inversely proportional, but had different changes among seven regions. (2) Since 1954, the overall trend in China has changed from wet to dry. Drought areas increased significantly (pu2009<u20090.05) during 1954–1983. Drought areas increased (not significantly) during 1984–2013, but the rate of drought/wetness clearly decreased (tendency rates were between −u20090.05 and 0.05/30xa0year, and accounted for 54.79% of China). (3) Dry/wet variation in North, Northeast, Central, and Southwest China played major roles. (4) Except in the North region, drought/wet conditions were affected by surface warming.

Projection of drought hazards in China during twenty-first century

Drought is occurring with increased frequency under climate warming. To understand the behavior of drought and its variation in the future, current and future drought in the twenty-first century over China is discussed. The drought frequency and trend of drought intensity are assessed using the Palmer Drought Severity Index (PDSI), which is calculated based on historical meteorological observations and outputs of the fifth Coupled Model Intercomparison Project (CMIP5) under three representative concentration pathway (RCP) scenarios. The simulation results of drought period, defined by PDSI class, could capture more than 90% of historical drought events. Projection results indicate that drought frequency will increase over China in the twenty-first century under the RCP4.5 and RCP8.5 scenarios. In the mid-twenty-first century (2021–2050), similar patterns of drought frequency are found under the three emission scenarios, and annual drought duration would last 3.5–4xa0months. At the end of the twenty-first century (2071–2100), annual drought duration could exceed 5xa0months in northwestern China as well as coastal areas of eastern and southern China under the RCP8.5 scenario. Drought is slightly reduced over the entire twenty-first century under the RCP2.6 scenario, whereas drought hazards will be more serious in most regions of China under the RCP8.5 scenario.