Variations In Forcing Research Articles

Agricultural drought over water-scarce Central Asia aggravated by internal climate variability

A severe agricultural drought swept Central Asia in 2021, causing mass die-offs of crops and livestock. The anthropogenic contribution to declines in soil moisture in this region over recent decades has remained unclear. Here we show from analysis of large ensemble simulations that the aggravation of agricultural droughts over southern Central Asia since 1992 can be attributed to both anthropogenic forcing and internal variability associated with the Interdecadal Pacific Oscillation (IPO). Although the negative-to-positive phase transition of IPO before 1992 offset human-induced soil moisture decline, we find that the positive-to-negative phase transition thereafter has doubled the externally forced rate of drying in the early growing season. Human-induced soil moisture loss will probably be further aggravated in the following century due to warming, albeit with increasing precipitation, and our simulations project that this trend will not be counterbalanced by the IPO phase change. Instead, this internal variability could modulate drying rates in the near term with an amplitude of −2 (+2) standard deviation of the IPO trend projected to amplify (weaken) the externally forced decrease in surface soil moisture by nearly 75% (60%). The findings highlight the need for the interplay between anthropogenic forcing and the natural variability of the IPO to be considered by policymakers in this climate-sensitive region.

Recent change in summer rainfall over the Tibetan Plateau: roles of anthropogenic forcing and internal variability

Recent change in summer rainfall over the Tibetan Plateau: roles of anthropogenic forcing and internal variability

Ocean tides in the colombian basin and atmospheric contribution to S2

The tidal behavior in the Colombia basin is described based on the analysis of eighteen tide gauge time series, fourteen in the Colombian coasts and four placed in neighboring countries. Tidal constituents are published for the first time at nine of these stations. Harmonic analysis shows that the main constituents in the Caribbean correspond to three diurnals (K1, O1, P1); three semidiurnals (M2, N2, S2), and one long period harmonic (Mf), showing amplitudes and phase lag that correspond to previous tidal reports in the basin. In Turbo, due to the shallow and extensive continental shelf in the Urabá gulf, M2 is amplified, and shallow water harmonics appear. The amplitude and phase of each observed constituent are compared with global tide models FES2014, TPXO9 and DTU10, showing good agreement. The most significant differences occur with semidiurnal harmonics at stations close to the amphidromic point in the eastern Caribbean. In Mf, considerable interannual variations are found, supporting the need of over one year of sea level data to assess this constituent in the Colombia basin accurately. The radiational component of S2 is assessed using barometric pressure in thirteen stations, confirming its importance when compared to the gravitational contribution to the observed sea level harmonic. A trend in the atmospheric S2 is found in Cartagena, which supports those trends in sea-level S2, previously reported in the Caribbean Sea, are caused by variations in the radiational forcing.

Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

Development of recommendations for optimizing the geometry of tower cranes

Problem. Special attention is paid to maintaining tower cranes in good working order and serviceable condition through their proper operation. Unfortunately, violation of the operating rules can lead to an emergency situation. In this case, both the crane operator and other workers on the construction site may be injured. And in some cases even to death. Therefore, the development of measures for safe operation is very important. It is also important to ensure sufficient strength of the load-bearing steel structures, which make up the bulk of the tower cranes, the breakdown of which leads to significant costs to restore the working condition of the tower. Goal. The goal of the work is to develop a method of interactive graphical modeling of three-dimensional models of metal structures with the use of a forced variation of their dimensions in the environment of computer-aided design – Autodesk Inventor package. Methodology. The solution of the set tasks implied the use of computer modeling methods with the use of modern 3-D modeling programs. Experimental studies were also carried out to determine loading modes. Results. A comprehensive methodology has been developed that uses the method of interactive graphical modeling of three-dimensional models of metal structures with a forced variation of their dimensions in the computer-aided design environment - Autodesk Inventor. It allows you to obtain rational parameters of the main elements of the tower crane – tower, boom, and others. This technique significantly increases the productivity and quality of the designer's work and reduces the time for research (tower cranes) and design documentation. Originality. The method of interactive graphic modeling of three-dimensional models of metal structures on the example of the tower crane with the use of a forced variation of their dimensions in the computer-aided design environment – Autodesk Inventor package is offered. Practical value. The use of the proposed methodology will significantly improve the efficiency of the design and operation of tower cranes.

Using Self-Organizing Map Algorithm to Reveal Stabilities of Parameter Sensitivity Rankings in Microbial Kinetic Models: A Case for Microalgae

Microalgae are multi-purpose microbial agents due to their capability to efficiently sequester carbon dioxide and produce valuable biomass such as protein and single-cell oils. Formulation and tuning of microalgae kinetics models can significantly contribute to the successful design and operation of microalgae reactors. This work aimed to demonstrate the capability of self-organizing map (SOM) algorithm to elucidate the patterns of parameter rankings in microalgae models subject to stochastic variations of input forcing functions–bioprocess influent component concentration levels. These stochastic variations were implemented on a modeled chemostat with a deterministic microalgae kinetic model consists of ten time-dependent variables and eighteen model parameters. The methodology consists of two major stages: (1) global sensitivity analysis (GSA) on the importance of model parameters with stochastic sampling of bioreactor influent component concentrations, and (2) training of self-organizing maps on the datasets of model parameter rankings derived from the GSA indices. Results reveal that functional principal components analysis can project at least 99% of the time-dependent dynamic patterns of the model variables on B-splines basis functions. The component planes for hexagonal lattice SOMs reveal that the sensitivity rankings some parameters in the algae model tested can be stable over a wide range of variations in the levels of influent component concentrations. Therefore, SOM can be used to reveal the trends in multi-dimensional data arrays arising from the implementation of GSA of kinetic models under stochastic perturbation of input forcing functions.

Improving multi-decadal coastal shoreline change predictions by including model parameter non-stationarity

Our ability to predict sandy shoreline evolution resulting from future changes in regional wave climates is critical for the sustainable management of coastlines worldwide. To this end, the present generation of simple and efficient semi-empirical shoreline change models have shown good skill at predicting shoreline changes from seasons up to several years at a number of diverse sites around the world. However, a key limitation of these existing approaches is that they rely on time-invariant model parameters, and assume that beaches will evolve within constrained envelopes of variability based on past observations. This raises an interesting challenge because the expected future variability in key meteocean and hydrodynamic drivers of shoreline change are likely to violate this ‘stationary’ approach to longer-term shoreline change prediction. Using a newly available, multi-decadal (28-year) dataset of satellite-derived shorelines at the Gold Coast, Australia, this contribution presents the first attempt to improve multi-decadal shoreline change predictions by allowing the magnitude of the shoreline model parameters to vary in time. A data assimilation technique (Ensemble Kalman Filter, EnKF) embedded within the well-established ShoreFor shoreline change model is first applied to a 14-year training period of approximately fortnightly shoreline observations, to explore temporal variability in model parameters. Then, the magnitudes of these observed non-stationary parameters are modelled as a function of selected wave climate covariates, representing the underlying seasonal to interannual variability in wave forcing. These modelled time-varying parameters are then incorporated into the shoreline change model and tested over the complete 28-year dataset. This new inclusion of non-stationary model parameters that are directly modelled as a function of the underlying wave forcing and corresponding time scales of beach response, is shown to outperform the multi-decadal predictions obtained by applying the conventional stationary approach (RMSEnon-stationary = 11.1 m; RMSEstationary = 254.3 m). Based on these results, it is proposed that a non-stationary approach to shoreline change modelling can reduce the uncertainty associated with the misspecification of physical processes driving shoreline change and should be considered for future shoreline change predictions.

Anthropogenic and internal drivers of wind changes over the Amundsen Sea, West Antarctica, during the 20th and 21st centuries

Abstract. Ocean-driven ice loss from the West Antarctic Ice Sheet is a significant contributor to sea-level rise. Recent ocean variability in the Amundsen Sea is controlled by near-surface winds. We combine palaeoclimate reconstructions and climate model simulations to understand past and future influences on Amundsen Sea winds from anthropogenic forcing and internal climate variability. The reconstructions show strong historical wind trends. External forcing from greenhouse gases and stratospheric ozone depletion drove zonally uniform westerly wind trends centred over the deep Southern Ocean. Internally generated trends resemble a South Pacific Rossby wave train and were highly influential over the Amundsen Sea continental shelf. There was strong interannual and interdecadal variability over the Amundsen Sea, with periods of anticyclonic wind anomalies in the 1940s and 1990s, when rapid ice-sheet loss was initiated. Similar anticyclonic anomalies probably occurred prior to the 20th century but without causing the present ice loss. This suggests that ice loss may have been triggered naturally in the 1940s but failed to recover subsequently due to the increasing importance of anthropogenic forcing from greenhouse gases (since the 1960s) and ozone depletion (since the 1980s). Future projections also feature strong wind trends. Emissions mitigation influences wind trends over the deep Southern Ocean but has less influence on winds over the Amundsen Sea shelf, where internal variability creates a large and irreducible uncertainty. This suggests that strong emissions mitigation is needed to minimise ice loss this century but that the uncontrollable future influence of internal climate variability could be equally important.

Re-examining flow pathways over the Chukchi Sea continental shelf

Velocity records from 8 mooring sites (2010–2019) and trajectories of drogued drifters (2012–2020) are used to evaluate patterns of flow on the Chukchi Sea continental shelf, with an emphasis on the latter three years (2016–2019). Together, these data provided insight into the temporal and spatial variability of the currents over this shelf. These data extend previous observations by five years and include three previously unoccupied sites, two of which span the Central Channel. Bathymetry directs a significant portion of the northward flow in Central Channel eastward across the Chukchi shelf where it joins the coastal flow prior to exiting Barrow Canyon and entering the Beaufort Sea. In addition, shelf-wide volume transport estimated from three mooring sites located off Icy Cape is modified from earlier analysis and extended in time. The resulting transport is highly correlated with that flowing through Central Channel, with similar magnitude. Icy Cape transport varies seasonally with variations in atmospheric forcing, as well as inter-annually, with an annual low of 0.24 Sv (September 2011–August 2012) to a annual high of 0.64 Sv (September 2017–August 2018), and a 9-year average of 0.43 Sv, or approximately 40% of the flow through Bering Strait. This nearly decade-long Icy Cape volume transport record also exhibits an increasing, although not significant, trend of ∼0.03 Sv/year.

Wind influence on residual circulation in Patagonian channels and fjords

Circulation in estuaries is typically considered to be driven by horizontal density gradients, called gravitational circulation, but can be modified by other forcing such as the wind. Studies have shown that wind influence can either augment or counteract density-driven residual flows in estuaries, but the temporal and spatial extent of this influence remains unknown, particularly in deep and highly stratified fjords. This study aims to fill this gap by investigating the seasonal variability in residual circulation, wind forcing, and stratification in an extensive fjord and channel system in Chilean Patagonia. Observations of current velocity and wind are supplemented by atmospheric and hydrodynamic circulation models in the Moraleda Channel, a system that connects the fjord system of Northern Patagonia to the Pacific Ocean. Results show a marked seasonality, given that the surface outflow is reversed by wind forcing during the winter, a season characterized by strong southward (up-channel) winds. This produces a near-surface flow directed up-channel in the upper ∼40 m of the water column, in opposition to the horizontal pressure gradient. Cross-channel transects revealed that the vertical structure of the along-channel residual velocity was three-layer during the winter. The near-surface (<50 m) along-channel current on the eastern side of the channel followed the wind direction (southward). Between 50 and 100 m depth the flow was out-channel and below 100 m depth the flow was again in-channel. On the western side, the along-channel residual flow was two-layer, with near surface flow directed northward (against the wind) with a return flow below (deeper than 50 m). During the spring, northward (out-channel) winds dominated and the vertical structure of the along-channel residual velocity was two-layer, augmenting the horizontal pressure gradient, which stabilized the water column, enhancing stratification, inhibiting mixing and elevated gravitational circulation.

Why Pacific quasi-decadal oscillation has emerged since the mid-20th century

Pacific quasi-decadal oscillation (PQDO) is one component of the multi-time-scale tropical Pacific decadal variability, with a variability center in the equatorial central Pacific (ECP). PQDO has emerged since the 1950s and has a significant impact on decadal climate variability over Asia and North America and Pacific storms. However, why it has intensified since the 1950s remains unknown. Here we test two competing hypotheses, (1) 11-year solar cycle forcing and (2) internal variability arising from El Niño–Southern Oscillation (ENSO) asymmetry, by analyzing simulation results, including one fixed-forcing control (CTRL) experiment and four sensitive experiments with millennial spectral solar irradiance (SSI), obtained from the Community Earth System Model–Last Millennium Ensemble modeling project. The four-member ensemble-averaged SSI experiments suggest that 11-year solar irradiance forcing cannot excite PQDO without stratospheric amplification of solar forcing. By analyzing 144 years of observations and the CTRL experiment, we find that the PQDO is nonstationary, and consecutive La Niña-induced decadal variability can boost PQDO in the ECP. El Niño could induce decadal ENSO signals in the NINO3.4 region but not in PQDO regions. The negative phase of PQDO tends to follow the occurrence of multi-year La Niña. We suggest that the emergence of PQDO since the 1950s is mainly due to the increase in multi-year La Niña events.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ МУЛЬТИДЕКАДНОЙ ИЗМЕНЧИВОСТИ ГИДРОМЕТЕОРОЛОГИЧЕСКИХ ПАРАМЕТРОВ В ПОНТО-КАСПИЙСКИХ МОРЯХ

NCEP/NCAR reanalysis data for the period of 1948–2020 were used to detect, describe (in geographical sense) and compare the patterns of multidecadal variability of the hydrometeorological parameters (thermal, dynamic and humidity) in the Ponto-Caspian region (Black Sea, Sea of Azov and Caspian Sea). Polynomial approximations of the time series of the annual mean values indicated the non-linear nature and periodicity of the long-term variability of considered parameters. Being geographically closely located, these seas reveal significantly different patterns of variability. In the Black Sea two temporal intervals of an average duration of 20 and 40 years with multidirectional trends of hydrometeorological parameters changes have been detected, while in the Caspian Sea three such intervals lasting 10–25 years are identified. The North-Eastern wind regime prevails over the both basins. However, if in the Black Sea wind components act unidirectionally (air temperature rise/cooling coincides with the weakening/ strengthening of both components), then in Caspian they act the opposite way, with the major portion of heat advection provided by zonal (Eastern) transport. Phases of air warming/cooling in both basins coincide with a weakening/strengthening of this transport. The time lag between the shift of dynamic and thermal (as well as humidity) regimes is about 6–8 years, suggesting a leading role of the large-scale atmospheric forcing variations in the regional variability. Specific humidity is positively correlated with air temperature and decreases/increases during periods of cooling/warming. In contrast, relative humidity and precipitation rate variations are negatively correlated with air temperature trends. Over the Caspian Sea intensification of Eastern transport and associated cooling coincide with a situation when the North Atlantic Oscillation (NAO) index decreases to negative values, and the East Atlantic-West Russian pattern (EAWR) index is significantly positive. On the contrary, weakening of the Eastern transport and warming occur during a period of sharp strengthening of the NAO, coinciding with strongly negative values of the EAWR index. As for the Black Sea, the situation is controversial: the general pattern of variability in 1948–1992 was similar to that in the Caspian Sea and consistent with described combinations of indexes. However, after 1992 no pronounced trend in variability of NorthEastern transport have been observed.

Tropical Indian Ocean basin hydroclimate at the Mid- to Late-Holocene transition and the double drying hypothesis

The spatial pattern of Holocene climate anomalies is crucial to determining the mechanisms of change, distinguishing between unforced and forced climate variability, and understanding potential impacts on past and future human societies. The 4.2 ka event is often regarded as one of the largest and best documented abrupt climate disturbances of the Holocene. Yet outside the data-rich Northern Hemisphere mid-latitudes, the global pattern of climate anomalies is uncertain. In this study we investigate the spatial and temporal variability of the tropical Indian Ocean hydroclimate at the Mid- to Late-Holocene transition. We conducted Monte-Carlo principal component analysis, considering full age uncertainty, on ten high-resolution, precisely dated paleohydroclimate records from around the tropical Indian Ocean basin, all growing continuously or almost continuously between 5 and 3 kyr BP. The results indicate the dominant mode of variability in the region was a drying between 3.97 kyr BP (±0.08 kyr standard error) and 3.76 kyr BP (±0.07 kyr standard error) with dry conditions lasting for an additional 300 years in some records, and a permanent change in others. This drying in PC1, which we interpret as a proxy of summer monsoon variability, fits with a previously recognised tropic wide change in hydroclimate around 4.0 kyr BP. An abrupt event from 4.2 to 3.9 kyr BP is seen locally in individual records but lacks regional coherence.A lack of apparent 4.2 ka event in tropical Indian Ocean hydroclimate has ramifications for climate variability in the Indus valley, and for the Harappan civilization. Through a comparison of existing Indian subcontinent paleoclimate records, upstream climatic variability in the Indian Summer Monsoon and winter Westerly Disturbances source regions, and modern climatology, we present the “Double Drying hypothesis”. A winter rainfall drying between 4.2 and 3.9 kyr BP was followed by a summer rainfall drying between 3.97 kyr BP and at least 3.4 kyr BP. The Double Drying hypothesis provides more detailed climatic context for the Harappan civilization, resolves the cropping paradox, and fits the spatial-temporal pattern of urban abandonment. The consequences for the new Mid- to Late-Holocene Global Boundary Stratotype Section and Point in a stalagmite from Meghalaya are explored.

Roles of External Forcing and Internal Variability in Precipitation Changes of a Sub‐Region of the U.S. Mid‐Atlantic During 1979–2019

AbstractObservations of the seasonal mean precipitation averaged over a sub‐region of the U.S. mid‐Atlantic show significant increases in summer but no changes in winter during 1979–2019. Clarifying the drivers of these observed changes is important for understanding and interpreting future projections of regional precipitation by climate models. This study examines the relative effects of external forcing and internal variability in the observed multi‐decadal changes using the historical and RCP8.5/SSP585 simulations from 37 models in the Coupled Model Intercomparison Project phase 5 (CMIP5), 35 models in CMIP6, as well as large ensemble simulations from 6 models, each including more than 20 members. During 1979–2019, enhanced warming over land relative to ocean due to external forcing induces low pressure anomalies over land that contribute to the wetting summer. While similar wetting also occurs during winter, such effect is offset by the drying associated with the positive phase of the North Atlantic Oscillation (NAO) in recent decades. Internal variability produces large uncertainty in the multi‐decadal precipitation changes simulated by climate models through changes in large‐scale circulation associated with the NAO in winter and the North Atlantic Subtropical High in summer. These multi‐decadal relationships between precipitation and large‐scale circulation under internal variability also manifest on inter‐annual timescale. These relationships highlight enhanced moisture transport from the Gulf of Mexico and North Atlantic in winter and near the mid‐Atlantic coast in summer that modulates precipitation change in the study region.

Impacts of climate change on estuarine stratification and implications for hypoxia within a shallow subtropical system

Vertical density stratification often plays an important role in the formation and expansion of coastal hypoxic zones through its effect on near-bed circulation and vertical oxygen flux. However, the impact of future climate change on estuarine circulation is widely unknown. Here, we developed and calibrated a three-dimensional hydrodynamic model for Pensacola Bay, a shallow subtropical estuary in the northeastern Gulf of Mexico. Model simulations based on years 2013–2017 were applied to examine changes in salinity, temperature, and density under future climate scenarios, including increased radiative forcing (IR) and temperature (T), increased freshwater discharge (D), sea level rise (SLR), and wind intensification (W). Simulations showed that the impacts of climate change on modeled state variables varied over time with external forcing conditions. The model demonstrated the potential for sea level rise and increased freshwater discharge to episodically increase vertical density gradients in the Bay. However, increased wind forcing destabilized vertical gradients, at times reducing the spatial extent and duration of stable stratification. For time periods with low freshwater discharge, moderate increases in wind speed (10%) can destabilize density gradients strengthened by increased discharge (10%) and sea level rise (0.48 m). In contrast, destruction of strong density gradients that form near the mid-Bay channel following flood events requires stronger wind forcing. These results highlight the importance of considering natural variability in freshwater and wind forcing, as well as local phenomena that are generally unresolved by global climate models.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

Slowdown in Landfalling Tropical Cyclone Motion in South China

AbstractChina is one of the countries suffering the most tropical cyclone (TC) disasters, while changes in translation speed of TCs landfalling in China remain unclear. Here we found that the translation speed of landfalling TCs in south China during the boreal summer has significantly decreased by 0.3 m s−1decade−1 during 1979–2019, which is largely driven by a decadal deceleration in steering flow in the late 1990s. The slowdown in steering flow is found to be a balance of the opposite effects of the negative phase of the Pacific Decadal Oscillation and sea surface temperature (SST) warming in the Indian Ocean since the late 1990s. Given the recent Indian Ocean SST warming has been viewed as a footprint of external forcing, the results suggest that the effects of external forcing and natural variability on TC translation speed can offset each other.

Benchmarking Simulated Precipitation Variability Amplitude across Time Scales

Abstract Objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales.

Multi-model approach to integrate climate change impact on carbon sequestration potential of afforestation scenarios in Quebec, Canada

Afforestation of unproductive or currently non-forested territories can increase carbon land sinks and thus contribute to mitigate climate change. However, investments on large-scale afforestation could be risky because of the predicted effect of climate change on forest productivity of newly created plantations. The aim of this study was to assess the carbon sequestration and mitigation potential of afforestation scenarios with different species (Picea mariana, Picea glauca, Pinus banksiana, Pinus resinosa and Populus spp) on open woodlands and abandoned farmlands in the Province of Quebec (Canada) under different radiative forcing projections. We modelled carbon dynamics in these lands under three Representative Concentration Pathways projections (RCP 2.6, RCP 4.5, and RCP 8.5) over the 2021–2100 period. The forest gap model PICUS was used to model tree growth of afforested species as a function of the Representative Concentration Pathways 2.6, 4.5 and 8.5; these data were then used as input in the Carbon Budget Model – Canadian Forest Sector 3 to simulate the evolution of ecosystem carbon stocks and fluxes as a function of forest management and climate. Carbon transfer to harvested wood products, and carbon fluxes associated with product life cycles and substitution effects on markets, were also included in the analyses. Results showed that Pinus species responded more strongly to variations in radiative forcing than for the other simulated species. Overall, aboveground biomass was particularly altered by increased radiative forcing, which in turn reduced harvesting yield and transfers to wood processing and products. At the end of the simulation, despite the expected impacts of radiative forcing on ecosystems, afforestation scenarios on open woodlands with black spruce, white spruce, and jack pine can deliver carbon mitigation of 32% – 70% over the baseline scenario and 4% – 12% for red pine on abandoned farmlands and, hence, contribute to efforts to reduce GHG emissions, especially over the long term. Although climate change is expected to impact the growth of newly planted areas as part of afforestation efforts, the results of our study suggest that the choice of species to plant and the selected forest management strategy have a greater impact on carbon stocks than climate change itself. This study provides a better understanding of the dynamics of afforestation under climate change and whether investments in plantation can contribute to GHG reduction targets.

Interdecadal Changes of the South Asian High in CMIP5/6 and Projection of Its Future Changes

Abstract Different from the significantly lifted geopotential height (H) that exhibits an enhanced South Asian high (SAH) under global warming, the eddy geopotential height (H′) can objectively reflect the decadal decline of SAH in the late 1970s. In this study, the capability of CMIP5/6 models for simulating the decadal decline of the SAH is assessed. Both the CMIP5 and CMIP6 models can yield better simulations of H′ compared to H simulations with smaller model spreads. CMIP6 models demonstrate better performances than CMIP5 in the simulation of variability and decadal changes of the SAH, which is attributed to the more reasonable simulations of upper-tropospheric eddy temperature (T′) by CMIP6. There is a large uncertainty of the SAH projection, since the SAH displays obviously different variations between SSP1-2.6 and SSP5-8.5. The internal climate variability may play a more important role in influencing the decadal variations of SAH compared to the anthropogenic forcing in the future twenty-first century, although both the external forcing and internal climate variability are important for the historical decadal changes of SAH.