Empirical modelling of snow cover duration patterns in complex terrains of Italy

Snow cover duration is a crucial climate change indicator. However, measurements of days with snow cover on the ground (DSG) are limited, especially in complex terrains, and existing measurements are fragmentary and cover only relatively short time periods. Here, we provide observational and modelling evidence that it is possible to produce reliable time-series of DSG for Italy based on instrumental measurements, and historical documentary data derived from various sources, from a limited set of stations and areas in the central-southern Apennines (CSA) of Italy. The adopted modelling approach reveals that DSG estimates in most settings in Italy can be driven by climate factors occurring in the CSA. Taking into account spatial scale-dependence, a parsimonious model was developed by incorporating elevation, winter and spring temperatures, a large-scale circulation index (the Atlantic Multidecadal Variability, AMV) and a snow-severity index, with in situ DSG data, based on a core snow cover dataset covering 97 years (88% coverage in the 1907–2018 period and the rest, discontinuously from 1683 to 1895, from historical data of the Benevento station). The model was validated on the basis of the identification of contemporary snow cover patterns and historical evidence of summer snow cover in high massifs. Beyond the CSA, validation obtained across terrains of varying complexity in both the northern and southern sectors of the peninsula indicate that the model holds potential for applications in a broad range of geographical settings and climatic situations of Italy. This article advances the study of past, present and future DSG changes in the central Mediterranean region.

Prediction of Indian summer monsoon onset with high resolution model: a case study

India is an agro-based country, receives nearly 80% of the annual rainfall during the summer monsoon season, and has a vital socioeconomic security link. The start of the Indian rainy season is observed over the country's southern tip (Kerala) and is referred to as the monsoon onset. The Indian Summer Monsoon (ISM) onset is one of the key aspects and unique for each year. This paper aims the prediction of ISM onset for different years by using the operational numerical weather forecasts at medium-range time scales by using different atmospheric variables from UKMO, and NCMRWF deterministic (NCUM) and ensemble model (NEPS) weather forecasts. For predicting the 2018 onset, we have adopted objective methods like the rainfall criteria, daily circulation index, potential temperature, moisture transport etc. The results for an early onset year (2018) show that the models could predict the onset date agreeing with the observed India Meteorological Department (IMD) onset date of 29 May 2018 from both UKMO 5-day and NCUM 10-day forecasts. This study also emphasizes on the utilization of the medium-range numerical weather forecasts, providing enough time to plan the farming activities. We also present the models’ performance/ skill for assessing the onset dates for the delayed onset (2019) and timely onset (2020) monsoons with NCUM 10-days lead time, and the results agree with IMD dates. From the UKMO, NCUM, NEPS models’ rainfall forecast show that the onset date on 29 May 2018 as IMD. Similarly, the analysis from circulation index, potential temperature, vertically integrated moisture transport analysis, outgoing long wave radiation, tropospheric temperature index clearly shows the onset date agreeing with IMD observations. Similarly, for the late onset (2019) and timely onset (2020) years, the daily circulation index agrees with IMD observed onset dates. Finally, these results from the deterministic and probabilistic forecasts are consistent with the observed onset dates, and these medium range forecasts are highly helpful to compute the monsoon onset date with higher lead times (10 days) within the error of one day.

Persistent Anomaly Changes in High-Resolution Climate Simulations

Persistent anomalies (PAs) are associated with a variety of impactful weather extremes, prompting research into how their characteristics will respond to climate change. Previous studies, however, have not provided conclusive results, owing to the complexity of the phenomenon and to difficulties in general circulation model (GCM) representations of PAs. Here, we diagnose PA activity in ten years of current and projected future output from global, high-resolution (15-km mesh) time-slice simulations performed with the Model for Prediction Across Scales-Atmosphere (MPAS-A). These time slices span a range of ENSO states. They include high-resolution representations of sea-surface temperatures and GCM-based sea ice for present and future climates. Future projections, based on the RCP8.5 scenario, exhibit strong Arctic amplification and tropical upper warming, providing a valuable experiment with which to assess the impact of climate change on PA frequency. The MPAS-A present-climate simulations reproduce the main centers of observed PA activity, but with an eastward shift in the North Pacific and reduced amplitude in the North Atlantic. The overall frequency of positive PAs in the future simulations is similar to that in the present-day simulations, while negative PAs become less frequent. Although some regional changes emerge, the small, generally negative changes in PA frequency and meridional circulation index indicate that climate change does not lead to increased persistence of midlatitude flow anomalies or increased waviness in these simulations.

Changes and Drive Mechanism of Climate Extremes During Recent 60 Years in Qilian Mountains, Northwestern China

A interesting study of analyzing the temporal and spatial characteristics and the reason change of extreme climate indexes based on the daily precipitation and temperature data of 24 meteorological stations in Qilian Mountains from 1961 to 2017. The results showed that the interannual change of the warming index of extreme temperature was similar to that of the cold index of extreme temperature. All daily indexes of extreme precipitation except CWD passed the significance level test of 5%. All daily indexes of extreme precipitation except for CDD in Hexi inland river basin, Qaidam inland river basin and Yellow river basin showed an increasing trend. However, the increasing extent of CWD, R10MM, R20MM and R25MM in Yellow river basin was lower than that of Qilian Mountain. The warming range of the four indexes (TX10, TN10, TXN and TNN) decreased from south to north. The spatial distribution of PRCPTOT, SDII, RX1DAY, RX5DAY, R95 and R99 was similar in the Qilian Mountains. The central part of the Qilian Mountains was the area with larger increasing region, and the increase region decreased from inside to outside. TX10, TN10, ID, FD showed a significant negative correlation with altitude, while TXN, TNN showed a significant positive correlation with altitude. The changes of TX10, TN10, TXN, TNN, ID, FD and DTR were the most obvious in the high altitude area (> 2500m), and the changes of TN90, TX90, TXX, TNX and GSL were the most obvious in the low altitude area (< 2500m). Qilian Mountains, Hexi inland river basin and Qaidam inland river basin were greatly affected by the AMO, NTA, CAR, SCSSMI, SAMSMI and were slightly affected by the Nino4, NAO, NP, SOI, AO, MEI. Extreme precipitation days indexes of Yellow river basin is highly correlated with AO and SCSSMI. The effect of the circulation index of Atlantic multidecadal Oscillation, Tropical Northern Atlantic Index, Tropical Southern Atlantic Index, North Tropical Atlantic SST Index, Caribbean SST index on the extreme temperature warm index was stronger than that of extreme temperature cold index.

Spatiotemporal Variation of the Burned Area and Its Relationship with Climatic Factors in Central Kazakhstan

Central Asia is prone to wildfires, but the relationship between wildfires and climatic factors in this area is still not clear. In this study, the spatiotemporal variation in wildfire activities across Central Asia during 1997–2016 in terms of the burned area (BA) was investigated with Global Fire Emission Database version 4s (GFED4s). The relationship between BA and climatic factors in the region was also analyzed. The results reveal that more than 90% of the BA across Central Asia is located in Kazakhstan. The peak BA occurs from June to September, and remarkable interannual variation in wildfire activities occurs in western central Kazakhstan (WCKZ). At the interannual scale, the BA is negatively correlated with precipitation (correlation coefficient r = −0.66), soil moisture (r = −0.68), and relative humidity (r = −0.65), while it is positively correlated with the frequency of hot days (r = 0.37) during the burning season (from June to September). Composite analysis suggests that the years in which the BA is higher are generally associated with positive geopotential height anomalies at 500 hPa over the WCKZ region, which lead to the strengthening of the downdraft at 500 hPa and the weakening of westerlies at 850 hPa over the region. The weakened westerlies suppress the transport of water vapor from the Atlantic Ocean to the WCKZ region, resulting in decreased precipitation, soil moisture, and relative humidity in the lower atmosphere over the WCKZ region; these conditions promote an increase in BA throughout the region. Moreover, the westerly circulation index is positively correlated (r = 0.53) with precipitation anomalies and negatively correlated (r = −0.37) with BA anomalies in the WCKZ region during the burning season, which further underscores that wildfires associated with atmospheric circulation systems are becoming an increasingly important component of the relationship between climate and wildfire.

Modeling of Waste Flow in Industrial Symbiosis System at City-Region Level: A Case Study of Jinchang, China

Waste is increasingly used as a renewable resource. Industrial symbiosis is an innovative concept for more efficient use of waste streams within industrial complexes, with the aim of reducing the overall environmental impact of the complex. Industrial symbiosis plays a more important role in promoting green economic growth and building low-carbon cities. Based on the ecological theoretical framework, combined with Waste Flow Analysis (WFA), the material flow analysis (MFA) and production matrix methods were used as the core to construct the Industrial Symbiosis System Waste Flow Metabolism Analysis (ISSWFMA) model. In addition, taking the “Jinchang Model” as an example, a typical case selected by the National Development and Reform Commission of China’s regional circular economy development model, we conducted a refined quantitative study on the flow and metabolism of waste flow in the regional industrial symbiosis system at the City-Region level using the circulation degree index. The following conclusions were obtained from the study: The ISSWFMA model can better describe the flow and metabolism of waste streams in the industrial symbiosis system at the City-Region Level and can provide data and methods for storage management. As the internal industrial chain and the correlation between various departments continuously improved, the Circulation Index (CI) of solid waste, wastewater, and exhaust gas in the industrial symbiosis system of Jinchang City showed an overall increasing trend, the degree of recycling was continuously increasing, the industrial symbiosis ability was continuously enhanced, and the system structure was more complete. At the same time, based on the analysis of different wastes, the industrial symbiosis is developed at different stages; based on the analysis of solid wastes, the industrial symbiosis ability of Jinchang’s Industrial Symbiosis System has strengthened and accelerated the fastest from 2005 to 2010; based on the analysis of wastewater, the industrial symbiosis ability of the system strengthened slowly during the whole study period; and based on the analysis of exhaust gas, the industrial symbiosis ability of the system continued to strengthen rapidly during the whole study period. Finally, on the basis of further discussion on the selection of waste recycling paths, we proposed to give full play to the role of market mechanisms, and to build recycling areas and ecological areas by strengthening industrial symbiosis and its derived urban symbiosis to achieve the goals of natural resource conservation, ecological environment protection, and harmonious coexistence between human and nature.

Model evaluation for Heatwaves over South Korea in CMIP6 models

&lt;p&gt;In this study, we defined diagnostic indices to evaluate the CMIP6 models based on the heatwaves mechanisms of Korea presented in previous studies. Based on this, the simulation performance of the model was quantitatively evaluated using Relative Error (RE), Inter-annual Variability Skill-score (IVS), and Correlation Coefficient (CC). The REs in diagnostic indices are still large, especially in heat wave circulation index (HWCI) and Indian summer monsoon rainfall index (IMRI), which is mainly due to weak convective activity bias over the northwestern Pacific Ocean and the northwestern India. However, the IVSs in diagnostic indices have been improved overall in the CMIP6 compared to the CMIP5, especially in the IMRI. The CC between the daily maximum temperature (TMAX) and the diagnostic factors in the model is very higher in HWCI than other indices, indicating that the convective activity over the northwestern Pacific is very important in heat wave in Korea. As a result, the total ranking of the model performance for heatwaves in Korea suggested that EC-Earth3-Veg, EC-Earth3, and UKESM-1-0-LL ranked high in CMIP6.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-03410)&lt;/p&gt;

UK Climate Projections 2018 (UKCP18): progress towards more information on future weather

&lt;p&gt;UK Climate Projections 2018 (UKCP18) included land and marine projections and were published in 2018 to replace UKCP09. The land projections had three components, and all were designed to provide more information on future weather compared to UKCP09. The first component updated the UKCP09 probabilistic projections by including newer CMIP5 data and focussing on seasonal means from individual years rather than 30-year averages. The probabilistic projections represent the wider uncertainty. The second two components (global and regional projections) both had the aim of providing plausible examples of future climate, but at different resolutions.&lt;/p&gt;&lt;p&gt;The global projections were a combination of 13 CMIP5 models and a 15-member perturbed parameter ensemble (PPE) of coupled simulations for 1900-2100 using CMIP5 RCP8.5 from 2005 onwards. The PPE was provided at 60km atmosphere, quarter degree ocean and the large-scale conditions from twelve of the members were used to drive the regional model at both 12km and 2.2km resolution. These plausible examples are more useful for providing information about weather in a future climate to support a storyline approach for decision making.&lt;/p&gt;&lt;p&gt;The talk will present examples of new ways to use UKCP18 compared to UKCP09.&amp;#160; We will show how the global projections can be used to understand that the recent record winter daily maximum temperature in the UK in 2019 had a large contribution from internal variability and what this means for breaking the record in a warming climate. We also use an example from China to demonstrate one way to exploit information at different time scales, looking at how a circulation index, which is predictable and related to tropical cyclone landfall, changes in a future climate.&lt;/p&gt;&lt;p&gt;Finally, we show that while the enhanced resolution of the global and regional projections has improved our capability to provide climate information linked to the better representation of circulation, they lack diversity in some of the key drivers of future climate. Therefore, a key way forward will be to find an appropriate balance between the need for better diversity (e.g. multiple ensembles such as CMIP or PPEs) and the need for an appropriate resolution to retain this new capability.&lt;/p&gt;

Spatiotemporal Variability of Precipitation in Beijing, China during the Wet Seasons

To comprehensively evaluate the changes in precipitation patterns in the context of global climate change and urbanization, the spatiotemporal variability of precipitation during the wet seasons of 1981–2017 in Beijing was analyzed in this study using up-to-date daily and hourly precipitation data from observation stations. It was concluded that the average annual precipitation in wet seasons showed a downward trend, while the simple daily intensity index (SDII) showed an upward trend. Precipitation in the central urban area of Beijing showed obvious changes from 1981 to 2017; the average annual precipitation in the central urban area was almost as great as that in Miyun country after 2010, which was the storm center for the past three decades. The average annual maximum 3-h and 6-h precipitation in the 2010s was higher than the past three decades, especially in urban and suburban areas. In addition, the atmospheric circulation index, urbanization impact, and topography were all found to be important factors that affect the pattern of precipitation in Beijing.