Climate Forecast Research Articles

Influence of radiosonde observations on the sharpness and altitude of the midlatitude tropopause in the ECMWF IFS

Abstract. Initial conditions of current numerical weather prediction systems insufficiently represent the sharp vertical gradients across the midlatitude tropopause. Data assimilation may provide a means to improve tropopause structure by correcting the erroneous background forecast towards the observations. In this paper, the influence of assimilating radiosonde observations on tropopause structure, i.e., the sharpness and altitude, is investigated in the ECMWF's Integrated Forecasting System. We evaluate 9729 midlatitude radiosondes launched during 1 month in autumn 2016. About 500 of these radiosondes, launched on request during the North Atlantic Waveguide Downstream Impact Experiment (NAWDEX) field campaign, are used to set up an observing system experiment (OSE) that comprises two assimilation forecast experiments, one run with and one without the non-operational soundings. The influence on the tropopause is assessed in a statistical, tropopause-relative evaluation of observation departures of temperature, static stability (N2), wind speed, and wind shear from the background forecast and the analysis. Temperature is overestimated by the background at the tropopause (warm bias, ∼ 1 K) and underestimated in the lower stratosphere (cold bias, −0.3 K) leading to an underestimation of the abrupt increase in N2 at the tropopause. The increments (differences in analysis and background) reduce these background biases and improve tropopause sharpness. Profiles with sharper tropopause exhibit stronger background biases but also an increased positive influence of the observations on temperature and N2 in the analysis. Wind speed is underestimated in the background, especially in the upper troposphere (∼ 1 m s−1), but the assimilation improves the wind profile. For the strongest winds the background bias is roughly halved. The positive influence on the analysis wind profile is associated with an improved vertical distribution of wind shear, particularly in the lower stratosphere. We furthermore detect a shift in the analysis tropopause altitude towards the observations. The evaluation of the OSE highlights that the diagnosed tropopause sharpening can be primarily attributed to the radiosondes. This study shows that data assimilation improves wind and temperature gradients across the tropopause, but the sharpening is small compared with the model biases. Hence, the analysis still systematically underestimates tropopause sharpness which may negatively impact weather and climate forecasts.

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

Climate forecast skill and teleconnections on seasonal time scales over Central Africa based on the North American Multi-Model Ensemble (NMME)

Synoptic Analysis and Subseasonal Predictability of an Early Heatwave in the Eastern Mediterranean

Greece and the surrounding areas experienced an early warm spell with characteristics of a typical summer Mediterranean heatwave in mid-May 2020. The maximum 2 m temperature at Kalamata (southern Greece) reached 40 °C on 16 May and at Aydin (Turkey), it was 42.6 °C on 17 May. There was a 10-standard deviation positive temperature anomaly (relative to the 1975–2005 climatology) at 850 hPa, with a southwesterly flow and warm advection over Greece and western Turkey from 11 to 20 May. At 500 hPa, a ridge was located over the Eastern Mediterranean, resulting in subsidence. The aims of this study were (a) to investigate the prevailing synoptic conditions during this event in order to document its occurrence and (b) to assess whether this out-of-season heatwave was predictable on subseasonal timescales. The subseasonal predictability is not a well-researched scientific topic in the Eastern Mediterranean Sea. The ensemble global forecasts from six international meteorological centres (European Centre for Medium-Range Weather Forecasts—ECMWF, United Kingdom Met Office—UKMO, China Meteorological Administration—CMA, Korea Meteorological Administration—KMA, National Centers for Environmental Prediction—NCEP and Hydrometeorological Centre of Russia—HMCR) and limited area forecasts using the Weather Research and Forecasting model with the Advanced Research dynamic solver (WRF) forced by Climate Forecast System version 2 (CFSv.2; NCEP) forecasts were evaluated for lead times ranging from two to six weeks using statistical scores. WRF was integrated using two telescoping nests covering Europe, the Mediterranean basin and large part of the Atlantic Ocean, with a grid spacing of 25 km, and Greece–western Turkey at 5 km. The results showed that there were some accurate forecasts initiated two weeks before the event’s onset. There was no systematic benefit from the increase of the WRF model’s resolution from 25 km to 5 km for forecasting the 850 hPa temperature, but regarding the prediction of maximum air temperature near the surface, the high resolution (5 km) nest of WRF produced a marginally better performance than the coarser resolution domain (25 km).

Construction and characteristic analysis of background error covariance coupled with land surface temperature

Land surface temperature (LST) is the key variable in land–atmosphere interaction, having an important impact on weather and climate forecasting. However, achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging. This is because there is limited knowledge about the cross-component background error covariance (BEC) between LST and atmospheric state variables. This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables, and whether this relationship varies spatially and temporally. To this end, the BEC coupled with atmospheric variables and LST was constructed (LST-BEC), and its characteristics were analyzed based on the 2023 mei-yu season. The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height, and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature. The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background, and also have obvious diurnal variations. These results provide valuable information for strongly coupled land–atmosphere assimilation.摘要地表温度 (LST) 是涉及陆气相互作用的关键变量, 对天气气候预报具有重要影响. 不过在同化中实现LST与大气的协调分析却并不容易. 这是因为目前对跨陆气圈层的背景误差协方差 (BEC) 的了解较少. 本文旨在探究LST与大气的背景误差是否存在联系, 以及这种联系是否存在时空变化. 为此, 本研究构建了耦合大气变量和LST的BEC, 并基于2023年梅雨季分析了其特征. 总体特征表明: LST误差主要与大气温度相关; 随着大气高度的上升, 误差相关性逐渐减小; LST误差的标准差明显大于低层大气温度. “多雨日”和“少雨日”的时空特征表明, LST与低层大气温湿度误差的相关性及标准差均与天气背景密切相关, 且具有明显的昼夜变化. 上述结果可为后续陆气强耦合同化提供参考.

Physical characteristics of convective and non-convective cirrus clouds from CALIPSO data over the South China Sea

Studying the characteristics and mechanisms of convective and non-convective cirrus clouds over the South China Sea is vital for their impact on regional climate dynamics, and enhancing predictive models for weather and climate forecasts. This study utilizes eight years of CALIPSO data (from March 2007 to February 2015) to investigate convective and non-convective cirrus clouds. Explicit new insights include the observation that convective cirrus cloud samples are three times more numerous than non-convective cirrus clouds. Convective cirrus clouds are associated with humid conditions and demonstrate higher ice water content (IWC) values ranging from 10−3 to 10−1 g m−3, whereas non-convective cirrus clouds tend to be drier, exhibiting IWC values ranging from 10−4 to 10−3 g m−3. Both cirrus cloud types exhibit a maximum cloud fraction at 10°N. Convective cirrus reach their peak cloud fraction at an altitude of 14 km, while non-convective cirrus typically occur at altitudes between 15 and 16 km. The seasonal variability of the convective cirrus cloud fraction primarily reflects bottom-up positive specific humidity anomalies originating from convective activity, whereas the non-convective cirrus cloud fraction is influenced by top-down negative temperature anomalies.

Smallholder farmers’ perceived motivations for the adoption and implementation of climate information services in the Atwima Nwabiagya District, Ghana

This study investigated smallholder farmers’ perceived motivations for the adoption and implementation of climate information services (CIS) in the Atwima Nwabiagya District, Ghana. Specifically, the study answered the following research questions. (i) What is the perception of smallholder farmers on climate change and variability in the Atwima Nwabiagya District?; (ii) What are the CIS accessed and used by the smallholder farmers for farm management decisions?; (iii) What are the perceived motivations for the adoption and implementation of CIS by smallholder farmers?; and; (iv) What are the barriers confronting smallholder farmers in adopting and implementing CIS? Primary data was collected through questionnaire surveys with 200 farming households and four key informant interviews in four selected communities (Kobeng, Seidi, Wioso, and Nkawie Panin) in the study district. Results showed that smallholder farmers perceived climate change and variability through increased windstorms and thunderstorms, variable rainfall, and increased temperature patterns. The surveyed farmers also perceived that the climatic changes affect their agricultural activities adversely. However, the smallholder farmers accessed and used CIS including weather warnings (86%), daily weather forecasts (70%), and seasonal forecasts (60%) to manage climatic risks on the farm. Results also showed that the smallholder farmers were motivated by critical factors including improvement in the acquisition of proactive measures (92%), increased knowledge of climatic patterns that affect food stock (91%), and a reduction in loss of stock produced (88%) to access and use CIS. Despite this, the smallholder farmers were confronted with key barriers in their uptake and utilization of CIS. These were the timeliness of CIS (73%), a lack of weather symbols in the forecasts (65%), and inadequate information on seasonal forecasts for long-term planning (61%). The study concluded that the motivations for smallholder farmers using CIS in the study district may vary depending on their context, goals, and challenges. However, the general motivation is to enhance smallholder farmers’ farm planning and risk management by using weather and climate forecasts to make better decisions on crop selection, planting dates, input use, pest and disease control, irrigation, harvesting, and marketing.

Short-term forecasting method for prevailing orientation of leads in the Laptev Sea ice cover during winter-spring season

Sea ice leads are potential routes of easier navigation in the Arctic seas during winter-spring season. Forecasts of the prevailing orientation of ice leads are required for the early selection of the optimal route of a ship in ice. An automated method for short-term forecasting of the prevailing orientation of leads in the Laptev Sea is developed in AARI based on the analog method. Data sources for making a forecast include predictive fields of atmospheric pressure up to 3 days in advance and historical data on surface atmospheric pressure, ice drift and sea ice leads. The data on ice leads are obtained by interpreting SuomiNPP satellite images in a visible and infrared range at 375 m spatial resolution for the cold seasons 2016–2022. The ice drift data are freely available OSI SAF data. In order to make a forecast of the prevailing orientation of leads by a predictive atmospheric pressure field, it is necessary to select from an electronic data archive a historical field of atmospheric pressure that is the most similar to the predictive field. The lead characteristics on the selected date are taken as the predictive ones. Automated selection of an analog consists in searching for an atmospheric pressure field from the electronic data archive which has minimal metrics for the forecast date together with 2 previous days. The metric is calculated as the sum of the squared differences between the change in atmospheric pressure per 1 km in the latitudinal and meridional directions between the predictive atmospheric pressure field and the potential analog field. Actual lead data for a date of the selected analog may not be available due to thick clouds. In this case, the direction of the minor axis of the strain ellipse calculated from ice drift data is taken as the prevailing orientation of leads. The success ratio of diagnostic forecasts by the method developed is 72 %. Comparison of the success ratio of the diagnostic forecasts with the results of inertial and climatic forecasts demonstrates a high effectiveness of the method for cases of abrupt change in the direction of air flows over the Laptev Sea within the first 1–2 days.

Evaluating climate change scenarios in the white volta basin: A statistical bias-correction approach

This study provides a critical assessment of future climate scenarios in the White Volta Basin (WVB), an area heavily reliant on groundwater resources. With monthly model results for two Shared Socioeconomic Pathway Scenarios (SSP2-4.5 and 5–8.5), seven (7) Coupled Model Intercomparison Project Phase 6 (CMIP6) models with spatial resolution ranging from 1.125° × 2.8° were assessed. The study also considered, three 30-year time intervals, 1971–2013 for the Baseline (historical) climate, the 2020s (2020–2040) 2050s (2041–2070), and the 2080s (2071–2075) for the future climate scenarios. The Climate Change for Watershed Modeling (CMhyd) software was used for bias correction of these models, with observational data sourced from the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (NCEP CFSR) and 12 gridded climate stations. The bias-corrected models validated using R2, NSE, RMSE, PBIAS, and additional metrics, demonstrated good calibration results compared to observed data. Precipitation ensembles showed 97–99% R2, 94–99% NSE, 70–485 mm RMSE, and -9-5% PBIAS. Maximum and minimum bias corrected temperatures performance varied from 95 to 99% R2, 92–99% NSE, 0.01–0.07 RMSE, and 0.01–0.23 PBIAS, Overall, precipitation levels are expected to decline for SSP2-4.5, while under SSP5-8.5 are expected to increase until the 2080s across all scenarios in comparison to the baseline period. Maximum temperature will be considerably high under SSP5-8.5, with an estimated increase of around 4.3 °C/year in comparison to the reference period. The monthly average variation in the maximum temperature ranges from 0.62 to 2.43 0Cunder the SSP5-8.5 scenario. These findings reveal the potential impacts of climate change on agricultural productivity, groundwater recharge, and crucial information for the development of Ghana's National Determined Contributions (NDCs) and National Adaptation Plans (NAPs). Thus, emphasizing the need for comprehensive adaptation strategies to mitigate the climate change impact on water resources and ecosystem services.

A hybrid statistical downscaling prediction model with timescale decomposition for summer precipitation over Hainan Island, China

AbstractBy observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island.

Assessing the suitability of CFSR data for SWAT model hydrologic simulation of Kunthipuzha river basin, Kerala, India

Among the different inputs for the hydrological model, well distributed and precise precipitation datahas a crucial role in accurately simulating the various processes in a watershed. Poor distribution network of rain gauges and lack of precise precipitation data is one of the most important problems involved in many Indian watersheds. This study investigates the potential of using an alternate source of data for hydrologic modelling. The Climate Forecast System Reanalysis (CFSR) data is a global, high resolution, coupled atmosphere-ocean-land surface-sea ice system. Ithas been reported as an alternative option for solving the data deficiency of certain watersheds. The suitability of the CFSR to model the stream flow of Kunthipuzha river, flowing through the famous Silent Valley National Park in Kerala was assessed. The Soil and Water Assessment Tool (SWAT) model was made use of for the simulation of hydrologic process. The model was simulated using calibrated parameters in which CN2, ALPHA_BF and ESCO are the major factors affecting runoff.The developed model was run with observed and predicted meteorological data (CFSR) and the simulated results of stream flow were compared using Nash Sutcliffe Efficiency (NSE), Coefficient of determination (R2) and Root mean Square Error (RMSE). The NSE, R2 and RMSE obtained when observed data was usedfor modelling were 0.82, 0.85 and 29.25 respectively, whereas with CFSR data, the values were 0.70, 0.72 and 37.18 respectively. The streamflow modelled with SWAT using observed meteorological data wascloser to the measured streamflow as compared with that using CFSR data. The NSE and R2 obtained with CFSR data (0.7 & 0.72) indicates that gridded data (CFSR data) can perhaps be utilized in data scare regions with reasonable accuracy.

Assessment of the Sensitivity of the Mean Climate Simulation over West Africa to Planetary Boundary Layer Parameterization Using RegCM5 Regional Climate Model

This study evaluates the performance of two planetary boundary parameterizations in simulating the mean climate of West Africa using the Regional Climate Model version 5 (RegCM5). These planetary boundary parameterizations are the Holtslag scheme and the University of Washington scheme. Two sets of three one-year simulations were carried out at 25 km horizontal resolution with three different initial conditions. The first set of simulations used the Holtslag scheme (hereafter referred to as Hol), while the second used the University of Washington (UW) scheme (hereafter referred to as UW). The results displayed in this study are an average of the three simulations. During the JJAS rainy season, with respect to GPCP, both models overestimated total rainfall in the orographic regions. The UW experiment represented total rainfall fairly well compared to its counterpart, Hol. Both models reproduced convective rainfall well, with a relatively weak dry bias over the Guinean coast subregion. Globally, UW performed better than Hol in simulating precipitation. The pattern of near-surface temperature in both models was well reproduced with a higher bias with Hol than with UW. Indeed, the UW scheme led to a cooling effect owing to the reduction in eddy heat diffusivity in the lower troposphere contributing to reduce the bias. As a consequence, the height of the planetary boundary layer (PBL) was best simulated using the UW scheme but was underestimated compared to ERA5, while using the Hol scheme failed to capture the height of the PBL. This is coherent with the distribution of total cloud cover, which was better simulated with the UW scheme compared to the Hol scheme. This study shows that use of both planetary boundary parameterizations leads to a good simulation of most of the climatological characteristics of the West African region. Nevertheless, use of the UW scheme contributes to a better performance than use of the Hol scheme, and the differentiation between the two schemes is significant along the Guinea Coast and in orographic regions. In these topographically complex regions, UW appears to be more appropriate than Hol. This study emphasizes the importance of planetary boundary parameterizations for accurately simulating climate variables and for improving climate forecasts and projections in West Africa.

EfficientRainNet: Leveraging EfficientNetV2 for memory-efficient rainfall nowcasting

Rainfall nowcasting is critical for timely weather predictions and emergency responses, particularly in flood-prone areas. Existing models, while accurate, often require substantial computational resources. Addressing this challenge, our study introduces EfficientRainNet, a neural network that leverages mobile inverted residual linear bottleneck blocks for memory-efficient rainfall nowcasting. Our evaluation, conducted over the State of Iowa, demonstrates that EfficientRainNet achieves accuracy comparable to that of the widely adopted encoder-decoder convolutional GRUs, yet with a substantially reduced model complexity, possessing less than 6% of the trainable parameters of the encoder-decoder convolutional GRUs and less than 9% of those of the compared Small Attention UNet. This lightweight design opens the possibility for deployment on edge devices, offering a scalable and accessible solution for real-time rainfall prediction. The results suggest further potential for extending the application of EfficientRainNet across broader regions and varied climatic conditions, harnessing its computational efficiency for widespread climate monitoring and forecasting.

The Signal-to-Noise Paradox in Climate Forecasts: Revisiting Our Understanding and Identifying Future Priorities

The Signal-to-Noise Paradox in Climate Forecasts: Revisiting Our Understanding and Identifying Future Priorities

Harnessing the Power of Graph Representation in Climate Forecasting: Predicting Global Monthly Mean Sea Surface Temperatures and Anomalies

AbstractThe variability of sea surface temperatures (SSTs) is crucial in climate dynamics, influencing marine ecosystems and human activities. This study leverages graph neural networks (GNNs), specifically a GraphSAGE model, to forecast SSTs and their anomalies (SSTAs), focusing on the global scale structure of climatological data. We introduce an improved graph construction technique for SST teleconnection representation. Our results highlight the GraphSAGE model's capability in 1‐month‐ahead global SST and SSTA forecasting, with SST predictions spanning up to 2 years with a recursive approach. Notably, regions with persistent currents exhibited enhanced SSTA predictability, contrasting with equatorial and Antarctic areas. Our GNN outperformed both the persistence model and traditional methods. Additionally, we offer an SST and SSTA graph data set based on ERA5 and a graph generation tool. This GNN case study has shown how the GraphSAGE can be used in SST and SSTA forecasting, and will provide a foundation for further refinements such as adjusting graph construction, optimizing imbalanced regression techniques for extreme SSTAs, and integrating GNNs with other temporal pattern learning methods to improve long‐term predictions.

COVID Impacts Cause Critical Gaps in the Indian Ocean Observing System

Abstract Observing and understanding the state of the Indian Ocean and its influence on climate and maritime resources is of critical importance to the populous nations that rim its border. Acute gaps have occurred in the Indian Ocean Observing System, which underpins monitoring and forecasting of regional climate, since the start of the COVID pandemic. The pandemic disrupted the deployment and maintenance cruises for the observational array and also resulted in supply chain issues for procurement and refurbishment of equipment. In particular, the observational platforms that provide key measurements of upper ocean heat variability have experienced serious multiyear declines. There is now record-low data reporting and the platforms that are successfully reporting are old and quickly surpassing their expected period of reliable operation. The overall impact on the observing system will take a few years to fully comprehend. In the meantime, there is a critical need to document the gaps that have appeared over the past few years and how this will impact our ability to improve understanding and model representations of the real world that support regional weather and climate forecasts. The article outlines the expected slow road to recovery for the Indian Ocean Observing System, documents case studies of successful international collaborative efforts that will revive the observing system and provides guidelines for resilience from unexpected external factors in the future.

Rural households' livelihood adaptation strategies in the face of changing climate: A case study from Pakistan

Rural and agricultural communities' adaptation to climate change has gained significant attention owing to many countries' vulnerability to climate change risks. A similar trend has been witnessed in South Asia, a highly climate-vulnerable region, where research has grown dramatically considering the agriculture sector's vulnerability to climate-induced disasters. However, little attention has been paid to the adaptation of the livelihoods of rural households. This research, therefore, takes the case of Pakistan to explore livelihood adaptation strategies of rural households to climate change and investigate the factors that expedite or halt the adoption of livelihood diversification strategies. A multistage sampling design is used in this research, where 480 rural households from the Punjab province of Pakistan were selected and interviewed using stratified and random sampling approaches. A multivariate probit (MVP) regression model is employed to analyze the factors affecting households' adoption of livelihood adaptation strategies. The results show that besides adaptation of agronomic operations (agricultural adaptation strategies), rural households in the study area employed a wide range of strategies to adapt their livelihoods to climate change. These strategies include poultry and livestock farming, value addition of farm produce, trading of animals and farm commodities, small businesses (shops, etc.), daily wage labor, horticultural crop farming, and non-farming jobs. The estimates of the MVP model revealed that respondents' education, household size, income, access to a credit facility, access to farm advisory services, and access to climate forecasts have significantly influenced the choice of livelihood adaptation strategies. Based on these findings, this research recommends that the authorities should make efforts to improve farmers' understanding of the adaptation of climate change risks and educate them to adopt multiple livelihood options to improve the resilience of their livelihoods to climate-induced risks. This research has important policy implications for other countries with similar socio-economic features.

Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses

Abstract. A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the turbulent surface fluxes of sensible and latent heat in the Arctic. We analyse these effects utilising four global atmospheric reanalyses, ERA5, JRA-55, MERRA-2, and NCEP/CFSR (including both the NCEP Climate Forecast System Reanalysis (CFSR) and the NCEP Climate Forecast System Version 2 (CFSv2)), and evaluate their uncertainties arising from inter-reanalysis differences in SIC and in the sensitivity of the turbulent surface fluxes to SIC. The magnitude of the differences in SIC is up to 0.15 but typically around 0.05 in most of the Arctic over all four seasons. Orthogonal-distance regression and ordinary-least-squares regression analyses indicate that the greatest sensitivity of both the latent and the sensible heat flux to SIC occurs in the cold season, November to April. For these months, using daily means of data, the average sensitivity is 400 W m−2 for the latent heat flux and over 800 W m−2 for the sensible heat flux per unit of SIC (change in SIC from 0 to 1), with differences between reanalyses that are as large as 300 W m−2 for the latent heat flux and 600 W m−2 for the sensible heat flux per unit of SIC. The sensitivity is highest for the NCEP/CFSR reanalysis. Comparing the periods 1980–2000 and 2001–2021, we find that the effect of SIC on turbulent surface fluxes has weakened owing to the increasing surface temperature of sea ice and sea-ice decline. The results also indicate signs of a decadal-scale improvement in the mutual agreement between reanalyses. The effect of SIC on turbulent surface fluxes arises mostly via the effect of SIC on atmosphere–surface differences in temperature and specific humidity, whereas the effect of SIC on wind speed (via surface roughness and atmospheric-boundary-layer stratification) partly cancels out in the turbulent surface fluxes, as the wind speed increases the magnitudes of both upward and downward fluxes.

Why Is the Westward Rossby Wave Propagation from the California Coast “Too Fast”?

Abstract Past work has shown that interannual California coastal sea level variability is mostly of equatorial origin, and decades of satellite sea surface height (SSH) and in situ dynamic height observations indicate that this interannual signal propagates westward from the California coast as nondispersive Rossby waves (RWs). These observations agree with standard linear vertical mode theory except that even when mean flow and bottom topography are considered, the fastest baroclinic vertical mode RW in each case is always much slower (1.6–2.3 cm s−1) than the observed 4.2 cm s−1. This order-1 disagreement is only resolved if the standard bottom boundary condition that the vertical velocity w′ = 0 is replaced by perturbation pressure p′ = 0. Zero p′ is an appropriate bottom boundary condition because south of San Francisco the northeastern Pacific Ocean boundary acts approximately like an impermeable vertical wall to the interannual equatorial wave signal, and therefore equatorial quasigeostrophic p′ is horizontally constant along the boundary. Thus, if equatorial p′ = 0 at the bottom, then this condition also applies off California. The large-scale equatorial ocean boundary signal is due to wind-forced eastward group velocity equatorial Kelvin waves, which at interannual and lower frequencies propagate at such a shallow angle to the horizontal that none of the baroclinic equatorial Kelvin wave signal reaches the ocean floor before striking the eastern Pacific boundary. Off California this signal can thus be approximated by a first baroclinic mode with p′ = 0 at the bottom, and hence the long RW speed there agrees with that observed (both approximately 4.2 cm s−1). Significance Statement The California Current System is one of the most biologically rich and best-documented coastal regions in the world. In this region coastal sea level propagates westward from the coast at about 110 km month−1, slow enough to enable us to make large-scale ocean climate forecasts of the California Current ecosystem using coastal sea level. Although the westward speed seems slow, theoretically it is about double what we would expect. Offered here is an explanation of why this speed is “too fast” by linking the California wave signal to the equator, El Niño, and the shallow equatorial ocean response to the wind.

Understanding Constraints and Enablers of Climate Risk Management Strategies: Evidence from Smallholder Dairy Farmers in Regional South India

The adoption of effective coping strategies is crucial for successful adaptation to the impacts of climate change in the dairy sector. However, little attention has been paid to understanding the perceived constraints and motivations toward such strategies. A survey was conducted among 104 dairy farmers from three semi-arid regions of South India. The aim of the survey was to explore the dairy farmers’ perception of climate risk, how it impacts their dairy farming system, the coping strategies they employ, and the barriers they face when implementing these strategies. The survey also investigated the factors that facilitate the adoption of adaptation measures. The results indicate dairy farmers in the region perceive drought, pests and diseases, and high temperatures as the major risks associated with climate change, which has resulted in decreased dairy income, animal health problems, reduced fertility, and food intake problems for their cattle. In response to climate variability, dairy farmers have adopted various coping strategies. The most important strategies include buying livestock insurance, keeping low debt obligations, and growing drought-tolerant grass varieties. However, most farmers face significant constraints in adopting these and other strategies including a lack of climate forecast data, the high cost of adaptation activities, and weak institutional support. On the other hand, the key enabling factors that support the adoption of these strategies include milk production security, suitable feed growing conditions, and family interest. Most importantly, the study found that certain factors such as age, education, number of earning family members, annual milk production, monthly cattle expenses, and landholdings significantly influenced dairy farmers’ strategies for adapting to climate change. The study recommends that providing timely climate forecasts, implementing improved policies such as vaccination and cattle health services, and establishing strong institutional support systems can help dairy farmers become more resilient to climate change and protect their livelihoods.

Climate change increases threat to plant diversity in tropical forests of Central America and southern Mexico.

Global biodiversity is negatively affected by anthropogenic climate change. As species distributions shift due to increasing temperatures and precipitation fluctuations, many species face the risk of extinction. In this study, we explore the expected trend for plant species distributions in Central America and southern Mexico under two alternative Representative Concentration Pathways (RCPs) portraying moderate (RCP4.5) and severe (RCP8.5) increases in greenhouse gas emissions, combined with two species dispersal assumptions (limited and unlimited), for the 2061-2080 climate forecast. Using an ensemble approach employing three techniques to generate species distribution models, we classified 1924 plant species from the region's (sub)tropical forests according to IUCN Red List categories. To infer the spatial and taxonomic distribution of species' vulnerability under each scenario, we calculated the proportion of species in a threat category (Vulnerable, Endangered, Critically Endangered) at a pixel resolution of 30 arc seconds and by family. Our results show a high proportion (58-67%) of threatened species among the four experimental scenarios, with the highest proportion under RCP8.5 and limited dispersal. Threatened species were concentrated in montane areas and avoided lowland areas where conditions are likely to be increasingly inhospitable. Annual precipitation and diurnal temperature range were the main drivers of species' relative vulnerability. Our approach identifies strategic montane areas and taxa of conservation concern that merit urgent inclusion in management plans to improve climatic resilience in the Mesoamerican biodiversity hotspot. Such information is necessary to develop policies that prioritize vulnerable elements and mitigate threats to biodiversity under climate change.