Progress in Research on Homogenization of Climate Data

The observation data from ground surface meteorological stations is an important basis on which climate change research is carried out, while the homogenization of the data is necessary for improving the quality and homogeneity of the time series. This paper reviews recent advances in the techniques of identifying and adjusting inhomogeneity in climate series. We briefly introduce the results of applying two commonly accepted and well-developed methods (RHtest and MASH) to surface climate observations such as temperature and wind speed in China. We then summarize current progress and problems in this field, and propose ideas for future studies in China. Along with collecting more detailed metadata, more research on homogenization technology should be done in the future. On the basis of comparing and evaluating advantages and disadvantages of different homogenization methods, the homogenized climate data series of the last hundred years should be rebuilt.

Climate change and Aedes albopictus risks in China:current impact and future projection

Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.

The research of sea-land breeze (SLB) on the west coast of Bohai Bay based on field observation

Four automatic meteorological stations were set up in a line from beach to inland perpendicular to the west coast of Bohai Bay. Wind direction and velocity at altitudes of 2 m, 4 m and 12 m were surveyed with 10 minute intervals. On ＂Sea-Land Breeze＂ （SLB） days, the transition from sea breeze to land breeze was very evident in the study area. Direction of sea breeze was basically perpendicular to the coast and mainly from the ENE and E. Duration of sea breeze varied by the stations＇ distance to the coastline, and the near-coast wind velocity was larger than that of the inland and decreases as it reaches inland. There was increased development of SLB on sunny days than on overcast days. The term ＂Climatic Coastal Zone＂ can be defined for the area influenced by SLB, which reaches more than 74 km inland on a typical SLB day but less than 10 km on a non-typical SLB day.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies.The wideused statistical downscaling model(SDSM)software tools use multi-linear regression to extract linear relations between largescale and local climate variables and then produce high-resolution climate maps from sparse climate observations.The latest machine learning techniques(e.g.SRCNN,SRGAN)can extract nonlinear links,but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance.In this study,we proposed a novel hybrid RBF(Radial Basis Function)network by embedding several RBF networks into new RBF networks.Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling.To test the performance of our model,we generated high-resolution precipitation,air temperature and humidity maps from 34 meteorological stations in Bangladesh.In terms of three statistical indicators,the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression(MLR),Kriging interpolation or a pure RBF network.