Climate Forecast Models Research Articles

Estimating the Impact of a 2017 Smoke Plume on Surface Climate Over Northern Canada With a Climate Model, Satellite Retrievals, and Weather Forecasts

AbstractIn August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full‐factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free‐tropospheric smoke injection, a mass‐based aerosol scheme and comparatively high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with and without fire emissions, we estimated that for 16 days over an 850,000 km2 region, the smoke decreased planetary boundary layer heights by between 253 and 547 m, decreased downward shortwave radiation by between 52 and 172 Wm−2, and decreased surface temperature by between 1.5°C and 4.9°C, the latter spanning an independent estimate from operational weather forecasts of a 3.7°C cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect.

Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties

Abstract. Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot, total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq, 398 % for αice, 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %.

ПОВЕРХНОСТНЫЕ ГРАВИТАЦИОННЫЕ ВОЛНЫ В СЕВЕРНОЙ АТЛАНТИКЕ И В ДРЕЙФУЮЩЕМ ЛЬДУ АРКТИКИ

Sea ice in the Arctic is one of the main dynamic elements in the interacting atmosphere–ice–ocean system. Currently, theoretical and experimental studies of drifting and soldered ice are used to develop predictive models of the mechanical state of sea ice. This paper presents the results of an experimental study of physical and mechanical processes on the surface of the Arctic Ocean. Field work was carried out using the spatial placement of autonomous seismic stations directly on the drifting ice. Data on surface gravitational waves of the ocean, accompanied by periodic deformations of bending and shear in the ice, have been obtained. On the basis of amplitude spectra, the features of the process of propagation of storm waves of swell in the ice cover are considered and a comparative analysis with map of maximum heights of sea waves is performed. A significant factor is the use of microseismic oscillations recorded at coastal stations of the Greenland Sea to determine the time and place of a powerful storm. The spectrum of the wave field in the ice sheet indicates an increase in the frequency of oscillations due to the dispersion of the wave field. New data have been obtained on the large-scale mechanics of the occurrence of cracks in the ice cover and discrete flexural-gravity waves as one of the main indicators of ice destruction. The results of the study are important for improving weather and climate forecasting models, as well as for solving engineering problems on the Arctic shelf.

Deep Learning of Systematic Sea Ice Model Errors From Data Assimilation Increments

AbstractData assimilation is often viewed as a framework for correcting short‐term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short‐term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data‐driven model parameterization which can predict state‐dependent model errors. We undertake this problem using an ice‐ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982 and 2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea‐surface temperature, ice velocities, ice thickness, net shortwave radiation, ice‐surface skin temperature, sea‐surface salinity, as well as a land‐sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free‐running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.

Contrasting Intraurban Signatures of Humid and Dry Heatwaves over Southern California

Abstract Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 10° ± 3°C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraurban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 6° ± 0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action. Significance Statement Heatwaves are the leading cause of morbidity and mortality among all environmental hazards. Moreover, their frequency and intensity are on the rise due to climate change. Southern California is no stranger to extreme heat, but persistently humid heatwaves still test the adaptability limits of its residents. We find that the set of two contrasting heatwaves that afflicted Los Angeles in the summer of 2020 forms the perfect testbed for characterizing the impacts of humid versus dry heatwaves on urban environment. Because climate model forecasts and long-term observational trends point to more humid heatwaves in the future for Southern California, our results underscore the importance of including moist heat in extreme heat warning frameworks.

Impact of Soil Moisture in the Monsoon Region of South America during Transition Season

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

The extremely wet spring of 2022 in Southwest China was driven by La Niña and Tibetan Plateau warming

In the spring of 2022, an excessive amount of rainfall fell in Southwest China (SWC) under the background of frequent droughts in history. This extreme event occurred in the decaying phase of a La Niña event, and thus, presumably La Niña played a role in this extreme event. Based on observational diagnoses and model forecasts, the atmospheric circulation anomalies, contributions of remote forcing, and the predictability of this event were examined in this work. It is suggested that La Niña and the Tibetan Plateau upper-tropospheric warming are two major factors leading to the extreme event. In addition to the recognized impact of La Niña, the upper-tropospheric warming over the Tibetan Plateau modulates the Asian atmospheric circulation by inducing a northwest-southeast wave pattern extending from the Ural Mountains to the Indochina Peninsula via the western Tibetan Plateau. The meridional heat contrast associated with the Tibetan Plateau warming favors upward motion and excessive rainfall in SWC. The statistical connection between the SWC spring rainfall anomaly and the northwest-southeast wave pattern is confirmed by a climate model forecast. The model captured the wet pattern in SWC in spring 2022 in short (1–3 months) lead real-time predictions though there are biases in the area and severity. That may be due to that the model did not well capture the atmospheric circulation anomalies at the middle and high latitudes associated with the Tibetan Plateau upper-tropospheric warming. These results indicate that such an event is predictable to some extent if both the ENSO evolution and heat condition over the Tibetan Plateau can be well predicted.

Characteristics of tropical–extratropical cloud bands over tropical and subtropical South America simulated by BAM‐1.2 and HadGEM3‐GC3.1

AbstractTropical–extratropical cloud bands are common in South America (SAm), contributing significantly to the total rainy season precipitation. Thus, it is fundamental that climate and weather forecast models correctly represent them and their associated dynamic aspects. Adopting an event‐based framework, we evaluate the performance of two global models in simulating the observed cloud bands over SAm: the Brazilian Global Atmospheric Model version 1.2 (BAM‐1.2) and the Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1). Both models reproduce the main characteristics of cloud bands and the dynamical aspects leading to their development and persistence. Nonetheless, the biases in precipitation during simulated cloud bands contribute more than 50% of the bias in total precipitation in some regions. BAM‐1.2 simulates fewer but more persistent cloud bands than observed; HadGEM3‐GC3.1 simulates weaker cloud band activity during early summer and more persistent events after January than observed. In all models, the biases in cloud band events arise from the interaction between biases in the basic state and the synoptic‐scale regional circulation. In the basic state, stronger upper level westerlies over the midlatitude South Pacific support the propagation of longer and slower Rossby waves towards subtropical SAm, increasing the duration of the cloud band events. This bias interacts with negative biases in the upper level westerlies over subtropical SAm, increasing the wind shear, hindering the propagation of synoptic‐scale Rossby waves into lower latitudes, and resulting in biases in the cloud band location, intensity, and seasonality. The application in this study of an event‐based framework robust to differences in model resolution and complexity enables the identification of small but critical biases in circulation. These biases are linked to synoptic‐scale rainfall system biases and help to explain the season total rainfall model biases.

Adaptability analysis of snow in the Zhangjiakou competition zone of the Beijing Olympic Winter Games for the next 30 years

Study regionZhangjiakou competition zone (ZJK-CZ) of the Beijing Olympic Winter Games. Study focusUnder the extreme uncertainty of future climate change scenarios, a system solution capable of snow depth forecasting is urgently needed for the sustainable use of Olympic Winter Games venues and the regular organization of winter sports in the post-Winter-Olympic era. This study predicts the snow depth distribution and changes in the ZJK-CZ of the Beijing Olympic Winter Games for the next 30 years utilizing the GBEHM (Geomorphology-Based Ecohydrological Model) model, together with CMIP6 climate forecast model data, and analyzes the operation and profitability of the ZJK-CZ in the post-Winter-Olympic era. New hydrological insights for the regionThis study developed a new integrated simulation methodology for the climate risk assessment of Olympic Winter Games venues in the context of future climate change. The percent bias of the snow depth simulated based on the new integrated model was found to be only 3 %. In the next 30 years, the ZJK-CZ will need to be replenished with at least 190 mm of artificial snow annually to meet the minimum standards for suitable winter sports days and requires an average of at least 49 days of artificially assisted snowmaking per year to ensure proper operation and profitability. This study is expected to provide a climate risk assessment for the construction and operation of winter sports venues.

Predictability of China winter temperature under different intensities of La Niña events in seasonal climate forecast models

AbstractIn this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña.

Evaluation of the regional climate model for the forest area of Yunnan in China

Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

ПРИМЕНЕНИЕ МНОГОУРОВНЕВОЙ ПАРАМЕТРИЗАЦИИ ГОРОДСКИХ ЛАНДШАФТОВ В КЛИМАТИЧЕСКОЙ МОДЕЛИ WRF-ARW ДЛЯ СОЗДАНИЯ БАЗЫ ДАННЫХ О КЛИМАТЕ МЕГАПОЛИСА И ЕГО ПРИГОРОДОВ: ПЕРВЫЕ РЕЗУЛЬТАТЫ НА ПРИМЕРЕ МОСКОВСКОГО РЕГИОНА

The current development level of urban meteorology as a synthetic science that includes elements of climatology,ecology, urban studies, and computational mathematics requires the creation of adequate databases of meteorological parameters:only in this way can they be properly used for solving problems of both applied sciences and fundamental research.Therefore, obtaining information on the intra-urban distribution of air temperature, relative humidity, and wind speed mustmeet the most stringent quality criteria. The most modern method for obtaining high-resolution databases is hydrodynamic modeling with the use of regional weather and climate forecast models (WRF, COSMO_RU, ENVIRO-HIRLAM, etc.).However, for horizontal resolution of about a kilometer it is already necessary to use so-called multilevel parameterizationsof the urban surface layer of the atmosphere, which take into account the vertical structure within the urban area. For this purpose, it seems promising to adapt the most modern physically based parameterizations of the lower atmosphere, such as Building EnergyParameterization (BEP) and Building Energy Model (BEM), for the popular, freely distributed regional model WRF-ARW, whichhas been used in our study for the Moscow region (Moscow + Moscow Oblast). The article reviews the advantages of this approachand the results of the first computational experiments. We provide estimates of the accuracy of temperature and relative humiditysimulation for a full-fledged experiment on dynamic downscaling of ERA5 reanalysis data within the Moscow region for 2010 (as aperiod with notable seasonal temperature contrasts, including the infamous extreme heat wave over European Russia).

The stratosphere: a review of the dynamics and variability

Abstract. Large-scale, intra-seasonal to inter-annual variability of the stratosphere is reviewed. Much of the variability is dynamical and induced by waves emanating from the troposphere. It is largely characterized by fluctuations in the strength of the polar vortex in winter and a quasi-biennial oscillation in the equatorial winds. Existing theories for the variability are generally formulated in terms of wave–mean-flow interactions, with refinements due, in part, to teleconnections between the tropics and extratropics. Climate and seasonal forecast models are able to reproduce much of the observed polar stratospheric variability and are increasingly successful in the tropics too. Compared to the troposphere the models display longer predictability timescales for variations within the stratosphere. Despite containing just ∼17 % of the atmosphere's mass, the stratosphere's variability exerts a powerful downward influence on the troposphere that can affect surface extremes. The stratosphere is therefore a useful source of additional skill for surface predictions. However, a complete dynamical explanation for the downward coupling is yet to be established.

Uncertainty in case of lack of information: extrapolating data over time, with examples of climate forecast models

The basic scientific tool for predicting is called a “forecast model”, a mathematical model underpinned by observations. Generally, it is the evolution of some parameters of the present-day law(s) over time that are considered of fundamental importance in a specific case. The relevant available data are obviously limited to the past period of time, which is admittedly a limited period in most cases, when the law in question is considered valid and verified with sufficient precision − while no direct information is available about the future trend. A mathematical (set of) function(s) is extrapolated ahead over time to show present and next generations what they should be supposed to observe in the future. A problem arises from the fact that no (set of) mathematical function that could be used for a model is infinitely “flexible”, i.e. apt to “correctly” interpolate any cluster of data, and the less a data set is, the less the parameters of the function(s) are. A data consistency is considered good when there is a balance between a mere “copying” the behaviour over time (e.g. when a function has to follow a given profile) and a satisfactory “averaging” the behaviour, especially over longer periods of time, without “masking” changing points. Furthermore, the data uncertainty is an embellishment, which the information often lacks, provided with extrapolations. Instead of it, the data uncertainty must be taken into account, and appropriate information must always be provided, since the quality of the adjustment of the available data is crucial for the quality of the subsequent extrapolation. Therefore, the forecast should better consist of an area (typically increasing its width over time) where future determinations are assumed to fall within a given probability range. Thus, it should be perfectly clear that the extrapolation of the past data into the future, i.e. a current evaluation that can be propagated to next generations, is affected by a high risk and that careful precautions and limitations should be taken.

Predictability of a tornado environment index from El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation

Abstract. El Niño–Southern Oscillation (ENSO) modulates severe thunderstorm activity in the US, with increased activity expected during La Niña conditions. There is also evidence that severe thunderstorm activity is influenced by the Arctic Oscillation (AO), with the positive phase being associated with enhanced activity. The combined ENSO–AO impact is relevant for situations such as in early 2021, when persistent, strong positive and negative AO events occurred during La Niña conditions. Here we examine the relation of a spatially resolved tornado environment index (TEI) with ENSO and the AO in climate model forecasts of February, March, and April conditions over North America. Bivariate composites on Niño 3.4 and AO indices show that TEI predictability is high (strong signals and probability shifts) when the ENSO and AO signals reinforce each other and low when they cancel each other. The largest increase in the expected value and variance of TEI occurs when Niño 3.4 is negative, and the AO is positive. Signal-to-noise ratios are higher during El Niño–negative AO than during La Niña–positive AO, but probability shifts are comparable.

Simulation and Scaling of the Turbulent Vertical Heat Transport and Deep-Cycle Turbulence across the Equatorial Pacific Cold Tongue

AbstractMicrostructure observations in the Pacific cold tongue reveal that turbulence often penetrates into the thermocline, producing hundreds of watts per square meter of downward heat transport during nighttime and early morning. However, virtually all observations of this deep-cycle turbulence (DCT) are from 0°, 140°W. Here, a hierarchy of ocean process simulations, including submesoscale-permitting regional models and turbulence-permitting large-eddy simulations (LES) embedded in a regional model, provide insight into mixing and DCT at and beyond 0°, 140°W. A regional hindcast quantifies the spatiotemporal variability of subsurface turbulent heat fluxes throughout the cold tongue from 1999 to 2016. Mean subsurface turbulent fluxes are strongest (∼100 W m−2) within 2° of the equator, slightly (∼10 W m−2) stronger in the northern than Southern Hemisphere throughout the cold tongue, and correlated with surface heat fluxes (r2= 0.7). The seasonal cycle of the subsurface heat flux, which does not covary with the surface heat flux, ranges from 150 W m−2near the equator to 30 and 10 W m−2at 4°N and 4°S, respectively. Aseasonal variability of the subsurface heat flux is logarithmically distributed, covaries spatially with the time-mean flux, and is highlighted in 34-day LES of boreal autumn at 0° and 3°N, 140°W. Intense DCT occurs frequently above the undercurrent at 0° and intermittently at 3°N. Daily mean heat fluxes scale with the bulk vertical shear and the wind stress, which together explain ∼90% of the daily variance across both LES. Observational validation of the scaling at 0°, 140°W is encouraging, but observations beyond 0°, 140°W are needed to facilitate refinement of mixing parameterization in ocean models.Significance StatementThis work is a fundamental contribution to a broad community effort to improve global long-range weather and climate forecast models used for seasonal to longer-term prediction. Much of the predictability on seasonal time scales is derived from the slow evolution of the upper eastern equatorial Pacific Ocean as it varies between El Niño and La Niña conditions. This study presents state-of-the-art high-resolution regional numerical simulations of ocean turbulence and mixing in the eastern equatorial Pacific. The results inform future planning for field work as well as future efforts to refine the representation of ocean mixing in global forecast models.

Wave processes in the drifting ice of the Arctic Ocean in the MOSAiC expedition. Winter period

One of the main directions of theoretical and applied research in the Arctic is the study of physical and mechanical processes in the atmosphere — ice — ocean system. For this purpose, theoretical and experimental problems are solved. The paper employs the method of monitoring the state of drifting ice by means of autonomous seismic stations in the MOSAiC international expedition in 2019–2020. The method of remote registration of ice information with a discreteness of 100 Hz made it possible to obtain data on the processes of compression and crushing of ice of various temporal and spatial scales. The paper presents early findings on the development of physico-mechanical processes in the ice cover under the influence of wind, oceanic gravitational waves, compression and crushing phenomena during large-scale deformations in drifting ice. The amplitude-frequency spectra of surface gravitational waves obtained in this work provide sufficient reason for attributing the phenomena described to swell waves and infra-gravity waves that occur in the stormy areas of the oceans. New data have been obtained on low-frequency horizontally polarized waves caused by the compression of ice and movements along breaks in the cohesive ice cover. The article considers the possibilities of using instrumental monitoring of the occurrence and development of tidal compression and crushing in the drifting ice of the Arctic Ocean. The results obtained can be used to develop methods for predicting the state of ice in real time both in engineering tasks and for improving weather and climate forecasting models.

Contrasting climate signals across a Scots pine (Pinus sylvestris L.) tree-ring network in the Middle Volga (European Russia)

Since the late-19th century, the Middle Volga has played a major role in the supplying grains and other agricultural products to European Russia. The study area is located in the south of sub-boreal forest in the north and in the forest-steppe in the south. Due to large seasonal differences in rainfall, agriculture in the region, especially in its southern part, strongly depends on hydroclimate variability. According to climate model forecasts, the frequency and intensity of droughts in the Middle Volga are expected to increase due to ongoing warming. Here we introduce 16 new Scots pine tree ring width (TRW) chronologies (Pinus sylvestris L.) from the region and use a dendroclimatological approach to determine what climatic factors drive radial growth. Our analysis revealed contrasting climate signals across the network of sites with chronologies from the north showing weak correlation with May temperature and precipitation (r = −0.27 and r = 0.28, respectively), while the southern sites demonstrated stronger relationships with climate in the first half of the vegetation season (May to July temperature, r = −0.26 to −0.43; May and July precipitation, r = 0.29–0.35). The northern sites did not demonstrate a strong growth response to the self-calibrated Palmer drought severity index (scPDSI) whereas the southern group was more drought sensitive had a strong drought response and positively correlates with scPDSI for the period from previous July to the current October (r = 0.27–0.56). Based on this strong relationship between southern TRW and scPDSI we reconstruct June-September scPDSI using the most sensitive sites (T04S, T06S, T08S) for the period from 1830 to 2014. The model explains 31% of variance. Our reconstruction shows droughts in 19th century: in 1831–33, 1851, 1853, 1859, 1863–65, 1880, 1891–92, 1897–98 and in 20–21th centuries: in 1906, 1921, 1936, 1939, 1967, 1975, 1996, 2010.

Opportunities for seasonal forecasting to support water management outside the tropics

Abstract. Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows.

Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa

AbstractSeasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium‐Range Weather Forecasts (ECMWF), UK Met Office (UK‐Met), Météo France, Deutscher Wetterdienst, and Centro Euro‐Mediterraneo sui Cambiamenti Climatici. The Multi‐Source Weighted‐Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead‐times and drought events, and compared to unweighted and skill‐weighted multi‐model ensemble mean forecast. For all models, the lead 1‐month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2‐month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1‐month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1‐month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK‐Met, although the multi‐model weighted average provides the highest performance compared to individual models and the un‐weighted multi‐model mean.