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This paper investigates the spatial inhomogeneity of the time-averaged, quasigeostrophic, double-gyre circulation response to fixed, realistic, large-scale modes of wind-stress forcing. While the companion paper of this study focused on understanding the anatomy of low-frequency, midlatitude climate variability in an idealised, eddy-resolving coupled model, this paper looked at understanding the nature of the wind-induced ocean gyre response using an ocean-only configuration of the same model. Our analysis revealed two, time-averaged responses to an east–west dipole, wind-stress curl anomaly in the ocean basin. Firstly, wind-stress anomalies in the western ocean basin led to changes in relative strength of the inertial recirculation zones and jet-axis tilt. This is consistent with an advection-dominated, nonlinear adjustment of the ocean gyres to anomalous forcing. Secondly, wind-stress curl anomalies in the eastern ocean basin was found to induce a largely independent response involving meridional shifts of the western boundary current extension (WBCE). The effects of time-averaged advection in this region are weak and the discovery of westward-propagating Rossby waves along the WBCE revealed the response is more akin to a baroclinic Rossby wave adjustment.

Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut fur Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD.

The standard framework of predictability defines a variable to be unpredictable from a set of observations if it is independent of those observations. This definition requires comparing two distributions: a forecast distribution that is conditioned on observations, and a climatological distribution that is not. However, if the system is non-stationary because of externally forced climate changes, or is characterized by a climatological distribution that is much broader than the distribution of states over the recent past, then a rigorous application of this framework gives unsatisfying answers to reasonable questions about weather and climate predictability. This paper proposes generalizations of this framework that resolves these limitations and is consistent with the definition of independence. The first generalization, which was proposed effectively by Lorenz and Leith, is to distinguish initial-value predictability from forced predictability, where the latter is defined by time variations in the climatological distribution. This paper goes a step further by introducing a new measure, called total climate predictability, that can be decomposed into a sum of previously known measures of forced and initial-value predictability, namely relative entropy and mutual information. The second generalization, called generalized predictability, provides a new approach to filtering in such a way that processes with long time scales do not contribute to predictability. This generalization is important when the system’s climatological distribution is much broader than the range of climates experienced in the recent past. These concepts are illustrated using a simple model in which all aspects of predictability can be solved exactly.

Stratospheric Aerosol Geoengineering (SAG) is one of the solar geoengineering approaches that have been proposed to offset some of the impacts of anthropogenic climate change. Past studies have shown that SAG may have adverse impacts on the global hydrological cycle. Using a climate model, we quantify the sensitivity of the tropical monsoon precipitation to the meridional distribution of volcanic sulfate aerosols prescribed in the stratosphere in terms of the changes in aerosol optical depth (AOD). In our experiments, large changes in summer monsoon precipitation in the tropical monsoon regions are simulated, especially over the Indian region, in association with meridional shifts in the location of the intertropical convergence zone (ITCZ) caused by changes in interhemispheric AOD differences. Based on our simulations, we estimate a sensitivity of − 1.8° ± 0.0° meridional shift in global mean ITCZ and a 6.9 ± 0.4% reduction in northern hemisphere (NH) monsoon index (NHMI; summer monsoon precipitation over NH monsoon regions) per 0.1 interhemispheric AOD difference (NH minus southern hemisphere). We also quantify this sensitivity in terms of interhemispheric differences in effective radiative forcing and interhemispheric temperature differences: 3.5 ± 0.3% change in NHMI per unit (Wm−2) interhemispheric radiative forcing difference and 5.9 ± 0.4% change per unit (°C) interhemispheric temperature difference. Similar sensitivity estimates are also made for the Indian monsoon precipitation. The establishment of the relationship between interhemispheric AOD (or radiative forcing) differences and ITCZ shift as discussed in this paper will further facilitate and simplify our understanding of the effects of SAG on tropical monsoon rainfall.

This paper investigates the impact of equatorial wind stress on the equatorial Ekman transport during the Indian Ocean Dipole (IOD) mature phase. The results show that the equatorial zonal wind stress directly drives the meridional motion of seawater at the upper levels. In normal years, the zonal wind stress south of the equator is easterly and that north of the equator is westerly, which contributes to southward Ekman transport at the upper levels to form the climatological Indian Ocean shallow meridional overturning circulation. During the years of positive IOD events, abnormal easterly winds near the equator bring southward Ekman transport south of the equator while they bring northward Ekman transport north of the equator. This causes the seawater to move away from the equator and hence induces upwelling near the equator, which forms a pair of small circulation cells that are symmetric about the equator at the upper levels (approximately 100 m deep). The abnormal circulation cell south (north) of the equator strengthens (weakens) the southward (southward) motion south (north) of the equator. During years with negative IOD events, the opposite occurs. In addition, during the mature period of IOD, the remote sea surface temperature anomaly (SSTA) such as El Niño–Southern Oscillation (ENSO) may exert some influence on Ekman transport anomaly near the equator during the mature period of IOD.

Model simulations suggest that Madden-Julian Oscillation (MJO) activity changes under the anthropogenic climate change background. However, satellite observations, which provide information of MJO convection activity, are not available before the 1970s, hindering research on the historical long-term variability of MJO. This study aims at extending the data length of MJO indices that include both MJO circulation and convection features, such as the widely used Real-Multivariate MJO (RMM) index, to the pre-satellite era. This paper introduces a new MJO index construction method, of which the outgoing longwave radiation (OLR) input is derived from upper-level geopotential, and names it as the Geopotential-based Multivariate MJO (GMM) index. The GMM index is derived over 1902–2008 and compared with the filtered version of RMM (FMM) index during 1981–2008. The GMM index is shown to (1) have the same climatological properties as the FMM index, (2) be statistically highly correlated to the FMM index, and (3) be able to indicate MJO activities and its convection features in the pre-satellite era. The overall bivariate correlation between FMM index and GMM index based on ERA-20C ranges from 0.959 to 0.968 in different phases. Evaluation results confirm the validity of the proposed MJO index construction method, which could capture MJO convection activity in the pre-satellite era and can be applied to all MJO indices that require input of OLR. This study provides an alternative way that overcomes the difficulty of historical MJO studies, and will be beneficial to our understanding of the long-term change of MJO.

This paper investigates the characteristics and causes for the interdecadal change in the relationships between early and late summer rainfall over South China (SC). This study finds that the correlations of the precipitation over SC between June and August shift from weakly positive in 1979 – 1995 to obviously negative in 1996-2019. Further analysis demonstrates that the interdecadal variations of monthly SST anomaly (SSTA) and associated air-sea interactions in June and August account for the decadal variations of the precipitation relationships. During the prior period 1979-1995, the tropical West Indian Ocean (WIO) shows a significant positive SSTA in June, which triggers Kevin waves and an anticyclone circulation over the tropical Northwest Pacific (NWP). The warm and wet air transported by the southwest airflow at the north of the anticyclone provides favorable environmental condition to produce more precipitation over SC region in June. In contrast, the SST dipole pattern with the negative SSTAs in the maritime continent (MC) and positive SSTAs in the tropical Central Pacific (CP) is dominant in August. The SST dipole pattern is inconducive to the formation of anticyclone over SC, causing a weak positive precipitation correlation between June and August. During the latter period 1996-2019, the precipitation over SC in June is the same as that in the prior period as there is no significant decadal change in tropical WIO SST and East Asian circulation. However, an opposite phase of the SST dipole anomaly pattern in MC and the tropical CP is dominant in August during the latter period. Accordingly, the positive feedback mechanism of air-sea interaction leads to the enhancement of local convection activities in MC and the meridional Hadley circulations and the NWP subtropical high, leading to a decrease of precipitation over SC in August. Overall, the decadal variation of the SST dipole anomaly pattern in MC and the tropical CP is the key factor affecting the adjustment of the correlations between June and August precipitation in the two periods.

For those involved in planning for regional and local scale changes in future climate, there is a requirement for climate information to be available in a context more usually associated with meteorological timescales. Here we combine a tool used in numerical weather prediction, the 30 weather patterns produced by the Met Office, which are already applied operationally to numerical weather prediction models, to assess changes in the UK Climate Projections (UKCP) Global ensemble. Through assessing projected changes in the frequency of the weather patterns at the end of the 21st Century, we determine that future changes in large-scale circulation tend towards an increase in winter of weather patterns associated with cyclonic and westerly wind conditions at the expense of more anticyclonic, settled/blocked weather patterns. In summer, the results indicate a shift towards an increase in dry settled weather types with a corresponding reduction in the wet and windy weather types. Climatologically this suggests a shift towards warmer, wetter winters and warmer, drier summers; which is consistent with the headline findings from the UK Climate Projections 2018. This paper represents the first evaluation of weather patterns analysis within UKCP Global. It provides a detailed assessment of the changes in these weather patterns through the 21st Century and how uncertainty in emissions, structural and perturbed parameters affects these results. We show that the use of these weather patterns in tandem with the UKCP projections is useful for future work investigating changes in a range of weather-related climate features such as extreme precipitation.

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using “convection-permitting” models to produce reliable climate change information over the Fenno-Scandinavian region.

Quantifying ecological memory could be done at several levels from the rate of physiological changes in an ecosystem all the way down to responses at the genetic level. One way of unlocking the information encoded in a collective environmental memory is to examine the recorded time-series data generated by different components of an ecosystem. In this paper, we probe into the case of the Great Barrier Reef (GBR) which is threatened by elevated sea surface temperatures (SST) and ocean acidification attributed to rising atmospheric CO2 levels. Specifically, we investigate the interrelated dynamics between the degradation of the GBR, SST, and rising atmospheric CO2 levels, by considering three datasets: (a) the mean percentage hard coral cover of the GBR from the archives of the Australian Institute of Marine Science; (b) SST close to the GBR from the National Oceanic and Atmospheric Administration; and (c) the Keeling curve for atmospheric CO2 levels measured by the Mauna Loa Observatory. We show that fluctuating observables in these datasets have the same memory behavior described by a non-Markovian stochastic process. All three datasets show a good match between empirical and analytical mean square deviation. An explicit analytical form for the corresponding probability density function is obtained which obeys a modified diffusion equation with a time dependent diffusion coefficient. This study provides a new perspective on the similarities of and interaction between the GBR’s declining hard coral cover, SST, and rising atmospheric CO2 levels by putting all three systems into one unified framework indexed by a memory parameter $$\mu $$ and a characteristic frequency $$\nu $$ . The short-time dynamics of CO2 levels and SST fall in the superdiffusive regime, while the GBR exhibits hyperballistic fluctuation in percent coral cover with the highest values for $$\mu $$ and $$\nu $$ .

Over time scales between 10 days and 10–20 years – the macroweather regime – atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation (fractional Gaussian noise) which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For one month lead time, our simple stochastic model shows similar values of the skill scores than the much more complex deterministic models.

Atmospheric blockings are generally associated with large-scale high-pressure systems that interrupt west-to-east atmospheric flow in mid and high latitudes. Blockings cause several days of quasi-stationary weather conditions, and therefore can result in monthly or seasonal climate anomalies and extreme weather events on the affected regions. In this paper, the long-term coupled CERA-20C reanalysis data from 1901 to 2010 are used to evaluate the links between blocking events over the North Atlantic north of 35° N, and atmospheric and oceanic modes of climate variability on decadal time scales. This study indicates more frequent and longer lasting blocking events than previous studies using other reanalyses products. A strong relationship was found between North Atlantic blocking events and North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO) and Baffin Island–West Atlantic (BWA) indices, in fall, winter and spring. More blocking events occur during the negative phases of the NAO index and positive phases of the BWA mode. In some situations, the BWA patterns provide clearer links with the North Atlantic blocking occurrence than with the NAO alone. The correlation between the synchronous occurrences of AMO and blocking is generally weak, although it does increase for a lag of about 6–10 years. Convergent cross mapping (CCM) furthermore demonstrates a significant two-way causal effect between blocking occurrences and the NAO and BWA indices. Finally, while we find no significant trends in blocking frequencies over the last 110 years in the Northern Hemisphere, these events become longer lasting in summer and fall, and more intense in spring in the North Atlantic.

The sawtooth-patterned glacial-interglacial cycles in the Earth’s atmospheric temperature are a well-known, though poorly understood phenomenon. Pinpointing the relevant mechanisms behind these cycles will not only provide insights into past climate dynamics, but also help predict possible future responses of the Earth system to changing CO$$_2$$ 2 levels. Previous work on this phenomenon suggests that the most important underlying mechanisms are interactions between marine biological production, ocean circulation, temperature and dust. So far, interaction directions (i.e., what causes what) have remained elusive. In this paper, we apply Convergent Cross-Mapping (CCM) to analyze paleoclimatic and paleoceanographic records to elucidate which mechanisms proposed in the literature play an important role in glacial-interglacial cycles, and to test the directionality of interactions. We find causal links between ocean ventilation, biological productivity, benthic $$\delta ^{18}$$ δ 18 O and dust, consistent with some but not all of the mechanisms proposed in the literature. Most importantly, we find evidence for a potential feedback loop from ocean ventilation to biological productivity to climate back to ocean ventilation. Here, we propose the hypothesis that this feedback loop of connected mechanisms could be the main driver for the glacial-interglacial cycles.

This paper presents an evaluation of climate simulations produced by the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) of the Center for Weather Forecast and Climate Studies (CPTEC). The model was run over the 1975–2017 period at two spatial resolutions, corresponding to ~ 180 and ~ 100 km, both with 42 vertical levels, following most of the Atmospheric Model Intercomparison Project (AMIP) protocol. In this protocol, observed sea surface temperatures (SSTs) are used as boundary conditions for the atmospheric model. Four ensemble members were run for each of the two resolutions. A series of diagnostics was computed for assessing the model’s ability to represent the top of the atmosphere (TOA) radiation, atmospheric temperature, circulation and precipitation climatological features. The representation of precipitation interannual variability, El Nino-Southern Oscillation (ENSO) precipitation teleconnections, the Madden and Julian Oscillation (MJO) and daily precipitation characteristics was also assessed. The model at both resolutions reproduced many observed temperature, atmospheric circulation and precipitation climatological features, despite several identified biases. The model atmosphere was found to be more transparent than the observations, leading to misrepresentation of cloud-radiation interactions. The net cloud radiative forcing, which produces a cooling effect on the global mean climate at the TOA, was well represented by the model. This was found to be due to the compensation between both weaker longwave cloud radiative forcing (LWCRF) and shortwave cloud radiative forcing (SWCRF) in the model compared to the observations. The model capability to represent inter-annual precipitation variability at both resolutions was found to be linked to the adequate representation of ENSO teleconnections. However, the model produced weaker than observed convective activity associated with the MJO. Light daily precipitation over the southeast of South America and other climatologically similar regions was diagnosed to be overestimated, and heavy daily precipitation underestimated by the model. Increasing spatial resolution helped to slightly reduce some of the diagnosed biases. The performed evaluation identified model aspects that need to be improved. These include the representation of polar continental surface and sea ice albedo, stratospheric ozone, low marine clouds, and daily precipitation features, which were found to be larger and last longer than the observed features.

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

The El Niño-Southern Oscillation (ENSO) drives substantial variability in precipitation and drought risk over Australia. Understanding the combined effect of anthropogenic forcing and ENSO on Australian precipitation extremes over the coming century can assist adaptation efforts. Here we use 24 CMIP5 climate models to examine externally forced changes in the frequency of “droughts”, when precipitation falls below the pre-industrial Decile 1 threshold. We focus on June to November (i.e., southern hemisphere Winter–Spring season) because precipitation during this period is important for agricultural production and recharging reservoirs in many parts of the country. The analysis in this paper is based on two 90-year simulations (1900–1989 and 2010–2099) for Historical and RCP8.5 scenarios. We show that the frequency of droughts, including droughts occurring in consecutive Winter–Spring seasons, is projected to increase in the twenty-first century under the RCP8.5 scenario in all eight Natural Resource Management (NRM) “clusters”. Approximately 60% of years are projected to be drought years in Perth, 35% in Adelaide, 30% in Melbourne, and approximately 20–25% of years in Sydney, Canberra and Brisbane. The relative frequency distributions of seasonally averaged Winter–Spring precipitation shift to lower values in all NRM clusters. However, apart from the Southern and Southwestern Flatlands, the shifts are accompanied by changes in the shape of the distributions whereby the high end of the distributions do not shift as much as other parts of the distribution and the wettest seasons become marginally wetter. This means that in most locations generally drier conditions are projected to be infrequently punctuated by seasons that are just as wet or wetter than the wettest years experienced during the twentieth century. While the models generally do a poor job in simulating ENSO precipitation teleconnections over Australia, an increase in ENSO-driven variability is suggested for the Wet Tropics, the Monsoonal North, the Central Slopes and the Southern and Southwestern Flatlands.

This study explores climate-change influences on future air pollution-relevant meteorological variables (e.g., temperature, wind, humidity, boundary layer heights) and atmospheric phenomena (e.g., heat wave, marine air penetration, droughts) over California by the 2050s. The Community Earth System Model simulation results from Coupled Model Intercomparison Project Phase 5 under an emission scenario that most closely aligns with California’s climate change goals were bias-corrected with respect to North American Regional Reanalysis data to reduce biases in both the climatological mean and inter-annual variations. The bias-corrected ~ 1° × 1° meteorological fields were dynamically downscaled to a resolution of 4 km × 4 km over California using the Weather Research and Forecasting model. This study focuses on summertime results, while the analysis of wintertime results will be presented in a separate paper. Our downscaled results projected a future increase of approximately 1 K in summer mean surface temperature over California under this single future climate realization. The temperature increase is larger in the nighttime than in the daytime. Water vapor mixing ratio is also projected to increase over California and off the coast. There are discernable decreases in boundary layer heights over the mountain ranges surrounding the central valley of California, while increases in boundary layer heights are observed over other regions in California. The number and duration of heat wave events are projected to increase substantially over the most populated parts of the State. The occurrence of marine air penetration events over the northern California is also projected to increase in the future.

An effective way of investigating the effects of tropical cyclones (TCs) on different spatial/temporal-scale environmental fields is to contrast the original circulation with the circulation from which the TCs have been removed. Although dynamical balance is required for analyzing TC contributions, the dynamic balance of TC-removed fields obtained by the existing TC removal method (which is widely used in the TC bogus procedure) is often ignored. In this paper, a TC removal method incorporating the potential vorticity (PV) inversion technique is proposed and its application to climate study is demonstrated. This method objectively detects the TCs’ positive PV disturbance, which is strong with a deep structure and overwhelmingly dominates the relatively weak and thin negative PV disturbance. The TC-removed field is well-balanced due to the dynamic balance consideration in the PV inversion framework. This approach isolates TC vortices, which are stronger and have a wider range of impacts compared with the TC components derived by the existing removal methods. The TC-removed fields obtained by the existing and the proposed methods are profoundly different, especially in dynamic balance. The TC contribution to intraseasonal variance and seasonal mean circulation in the tropical western North Pacific is examined. The existence of TCs enhances the amplitude and propagation of intraseasonal oscillation and strengthens the seasonal mean circulations such as the low-level monsoon trough and upper-level anticyclone in the region. Whereas the existing and the proposed TC removal techniques yield consistent results, the proposed technique yields larger TC contributions to the seasonal-mean circulation and the amplitude and northward propagation tendency of intraseasonal oscillation.

Based on a single-model 100-member ensemble of climate change simulations performed with the MPI-ESM, this paper investigates how the probability density function (PDF) of atmospheric general circulation (AGC)—a marginal distribution of the full climate PDF—responds to increasing $${\hbox {CO}}_2$$ concentration. The investigation provides a first assessment of $${\hbox {CO}}_2$$-induced changes in AGC-PDF derived from ensemble statistics (as oppose to time statistics in usual practice) that arise solely from internal variability consistent with the imposed $${\hbox {CO}}_2$$-forcing. By focusing on the first two moments of the AGC-PDF related to the Hadley circulation and the subtropical jets, we find that the major $${\hbox {CO}}_2$$-induced changes in the PDF are related to structure changes in the mean circulation and in the circulation variability. As $${\hbox {CO}}_2$$ concentration increases, different components of the AGC can co-evolve with changing forcing. In particular, the mean Hadley cell co-evolves with the mean subtropical jets. The co-evolution is characterized by the concurrent Hadley cell widening and poleward shifting of the subtropical jets (particularly strong in January), by a strengthening of both the Hadley cell and the subtropical jets in October, and by a weakening of the Hadley cell and a strengthening of the northern and southern jets in January. The changes in circulation variability are more subtle, and reveal only large and spatially coherent changes in places, where the variability is already strong without increasing $${\hbox {CO}}_2$$ concentration, such as at the northern and southern flanks of the summer southern subtropical jet, and at the northern flank of the southern polar night jet. There are some significant cross-variabilities between different components of the AGC. Overall, the $${\hbox {CO}}_2$$-induced AGC-changes can be described by linear trends in the first and second moments, with the trends in the means being much stronger than those in the variances and co-variances.

The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90 (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1 of the variance is at scales with k> 7. The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in k= 0 , in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Ocean-Land-Atmosphere Studies (COLA), and NOAA CPO organized a CFSv2 Evaluation Workshop in 2012. This topical collection includes a selection of peer-reviewed papers consisting of material presented at the workshop. The more than 20 papers in this collection have documented significant progress in the performance of CFSv2 in simulating ISI climate variability and predicting key climate variables, in comparison to the previous NCEP operational climate forecast system. The papers have also identified key model biases and deficiencies in predicting climate variables (such as precipitation and temperature), simulating the modes of climate variability and phenomena (such as El Nino and the Southern Oscillation or ENSO, the Madden– Julian Oscillation or MJO, the Arctic Oscillation or AO, global and regional drought, and monsoons) and physical processes and their interactions (such as cloud distributions, land–atmosphere interactions, and ocean–atmosphere interactions). This collection of papers is expected to provide insight into and guidance for the development of the next generation operational CFS. We’d like to express our sincere appreciation to Professor Edwin Schneider for his effort and dedication as the Chief Editor for this special collection. We are pleased to present the Topical Collection of the Climate Forecast System version 2 (CFSv2). The CFS is a coupled global climate model used for operational intraseasonal-to-interannual (ISI) prediction at the National Centers for Environmental Prediction (NCEP). NCEP developed CFSv2 over the course of several years and implemented it into operations in March 2011. External community research supported by the Climate Program Office (CPO) enhanced core internal development and evaluation. To bring the broader climate research and applications community together with NCEP scientists to evaluate the utility of CFSv2 for climate modeling research and as a climate forecast tool, the NCEP Climate Prediction Center (CPC), NOAA Climate Test Bed (CTB), Center for

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Cut-off Low (COLs) are often associated with heavy precipitation and strong wind events, but there are still uncertainties on how their identification affect the numbers and seasonality. This paper aims to determine the sensitivity of identifying Southern Hemisphere COLs in the ERA-Interim reanalysis to different types of identification criteria. Upper-level cyclones are initially tracked on the 300 hPa level using relative vorticity and geopotential in order to present different perspectives. This reveals significant differences between the numbers and length of the identified tracks for the two fields. To identify the COLs different post-tracking filters are applied which are divided into two steps. Firstly, three filters are considered to separate cut-off lows from open troughs by sampling winds at different offset radial distances from the upper-level cyclonic centres. Secondly, potential vorticity and temperature criteria are imposed to determine how these conditions affect the identified COLs in terms of numbers, seasonality and intensity. It was found that methods based on multiple criteria restrict the COL identification by imposing specific characteristics, while methods based on simpler schemes (e.g. using only winds) can detect larger samples of COLs observed visually in the geopotential maps. Although it is difficult to say which method is more accurate in identifying COLs, because of the subjective aspect of observer’s assessment, a scheme using only winds should be more representative of reality as this simply imposes on the detection system a cyclonic circulation appearance regardless of the physical and dynamical characteristics. Therefore, this type of method could be considered as a standard method for identifying COLs that can be used for either operational or research purposes.

Contributions to climate uncertainty in the modeling of the near surface temperature of the ocean come from several sources: the knowledge of the initial state of the atmosphere, the inadequacy of a model to include or correctly represent all aspects of the dynamics, the uncertain knowledge of the initial state of the ocean and small-scale (or internal, “weather”) noise. This paper concentrates on the latter uncertainty, the uncertainty in the knowledge of the initial field, resulting in a map of the uncertainty of the near surface temperature field. To make the full estimate of this uncertainty, the outcomes from a relatively small, 30 member, ensemble were expanded using statistical emulators. The uncertainty varies in space and decreases in time. There are broad areas of similar uncertainty, consistent with uncertainties in the initial conditions and the variability in local processes. After 10 years, the average spatial uncertainty calculated from this expanded data set gives a range of +/− 0.4 $$^{\circ }$$ C with 95% of the spatial areas having an uncertainty of less than +/− 0.9 $$^{\circ }$$ C. When compared to other estimates of similar metrics, the map represents a lower bound on the uncertainty because this first experiment examines the ocean/ice system in isolation from feedbacks that occur between the atmosphere and the ocean. This uncertainty also does not include any contribution from the lack of knowledge about how a future state of the air/ocean system will be forced by changes in greenhouse gases.

The real-time prediction skill for El Nino-Southern Oscillation has not improved steadily during the twenty-first century. One important reason is the season-dependent predictability barrier (PB), and another is due to the diversity of El Nino. In this paper, an approach to data analysis for predictability is developed to investigate the season-dependent PB phenomena of two types of El Nino events by using the monthly mean data of the preindustrial control (“pi-Control”) runs from several coupled model outputs in CMIP5 experiments. The results find that predictions for Central Pacific El Nino (CP-El Nino) suffered from summer PB, whereas those for Eastern Pacific El Nino (EP-El Nino) are mainly interfered with by spring PB. The initial errors most frequently causing PB for CP- and EP-El Nino are revealed and they emphasize that the initial sea temperature accuracy in the Victoria mode (VM) region in the North Pacific is more important for better predictions of the intensity of the CP-El Nino, whereas that in the subsurface layer of the west equatorial Pacific and the surface layer of the southeast Pacific is of more concern for better predictions of the structure of CP-El Nino. However, for EP-El Nino, the former is indicated to modulate the structure of the event, whereas the latter is shown to be more effective in predictions of the intensity of the event. Obviously, for predicting which type of El Nino will occur, more attention should be paid to the initial sea temperature accuracy in not only the subsurface layer of the west equatorial Pacific and the surface layer of the southeast Pacific but also the region covered by the VM-like mode in the North Pacific. This result provided guidance aiming at how to initialize model in predictions of El Nino types.

This two-part paper aims to provide a Lagrangian perspective of the final southern warming in spring of 2002, during which the stratospheric polar vortex (SPV) experienced a unique splitting. We approach the subject from a dynamical systems viewpoint and search for Lagrangian coherent structures using a Lagrangian descriptor that is applied to reanalysis data. Part I presents our methodology and focuses by means of a kinematic model, on the understanding of fundamental processes for filamentation and ultimately for vortex splitting on an isentropic surface in the middle stratosphere. The present Part II discusses the three dimensional (3D) evolution of the flow during the selected event. For this, we apply concepts developed in Part I concerning a definition of the vortex boundary that helps in the selection of trajectories to illuminate the evolving flow structures, and a criterion that allows to justify why at an isentropic level a pinched vortex will split in later times. Lagrangian structures identified include surfaces that are several kilometers deep, and which a particle trajectory analysis confirms as barriers to the flow. The role of Lagrangian structures in determining the fate of particles during the SPV splitting is discussed., Comment: 15 pages, 8 pages

The interdecadal Pacific Oscillation (IPO) represents the decadal mode of the El Nino-Southern Oscillation phenomenon. As such the IPO is one of the dominant modes of decadal climate variability on both sides of the Pacific Ocean basin. For this paper we utilized a newly developed tree-ring data network comprised of five multi-centennial Vietnamese cypress ring-width chronologies that range from around 11°N–23°N latitude. We combined these data with an average of tree-ring derived drought indices from the North American Drought Atlas over the south central USA, from a box that spans from 27.5°N–35.0°N, 85.0°–110.0°W and contains 55 gridpoints and more than 100 tree ring site chronologies, these 2 locations exhibit rainfall variations that are strongly, negatively correlated with each other and are representative of the influence of the IPO on terrestrial rainfall. The final reconstruction model, weighted most heavily on the three most southerly of the five Vietnamese cypress records, spans from 1350 to 2004, and explains nearly 57% of the variance in the original IPO data for the 5-month season of October–February. The reconstruction model passes all standard statistical tests using a split calibration-verification scheme. We reveal 15 positive and 15 negative phase shifts of the IPO prior to the period of instrumentation, suggesting that the IPO has been active for at least the past seven centuries with varying degrees of intensity. We compare our reconstruction with two related millennial records: the MacDonald and Case (Geophys Res Lett 32(8):L08703, 2005) tree ring-derived reconstruction of the Pacific Decadal Oscillation, and an ice core-derived reconstruction of the IPO from the Law Dome Ice core in Antarctica by Vance et al. (Geophys Res Lett 33(6):L06712, 2015). While there is good general agreement with the latter record, there are three key periods of the past where the two records are out of phase, and we explore the reasons for this disparity. Direct comparison with the related Tripole Index (TPI) shows weaker correlation, likely owing to the stronger relationship between our tree ring data and the equatorial Pacific region relative to the north and south regions of the Pacific that combine to comprise the TPI calculation.

© 2019, Regents of the University of California. Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of “tail dependence” arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data.

Recent studies have shown that spring land surface temperature (LST) and subsurface temperature (SUBT) over the high elevation areas in the western US (WUS) have significant impacts on the downstream summer droughts/floods in North America. In this paper, both the National Centers for Environmental Prediction—Global Forecast System (NCEP-GFS) general circulation model (GCM) and the weather research and forecasting (WRF) regional climate model (RCM) are employed, where RCM scenarios utilized initial and lateral boundary conditions derived from the corresponding NCEP-GFS scenarios. Here we use a late spring flood in the US Southern Great Plains (SGP) case to examine whether simulation of the LST/SUBT downstream effects is sensitive to the domain size choice, change in dynamical cores within the same model, as well as to the representation of surface processes parameterizations. Although all RCM experiments with different settings simulate reasonably geographical patterns of observed LST and precipitation anomalies, we found that the choice of the domain size is crucial for proper downscaling the LST/SUBT downstream effects to accurately produce the observed precipitation/LST anomalies over the SGP/WUS, respectively, along with the associated large-scale features. The southern boundary location has been identified to be crucial in producing the SGP Low Level Jet strength, which in turn brings more moisture from the Gulf of Mexico to the SGP and thereby resulting in a better simulation of the precipitation anomaly in that area. The sensitivity of the simulation of the LST/SUBT downstream effect to dynamical cores is assessed by inter-comparing the Non-hydrostatic Mesoscale Model (NMM) and the Advanced Research WRF dynamic cores. We find NMM was better at generating the large-scale eastward wave train, a crucial process associated with the LST/SUBT downstream effect. Meanwhile, this study also shows that the LST/SUBT downstream effects were not significantly dependent on the surface process parameterizations, although the Simplified Simple Biosphere model version 3(SSiB3) highlighted a better performance over SSiB2.

In this paper, we study the relationship between the covariability of the Indian summer monsoon (ISM) and East Asian summer monsoon (EASM) and the rainy season precipitation anomaly in the Mekong River basin (MRB) using ERA-Interim reanalysis data and precipitation from the Climate Hazards Group Infrared Precipitation station for 1981–2016. The results indicate that the interannual variability of the rainy season precipitation in the MRB is significantly modulated by the ISM and EASM covariability. Herein, the ISM mainly influences the rainy season precipitation west of the MRB, and the EASM mainly influences the rainy season precipitation over the southeastern MRB. When the Bay of Bengal (BOB) and South China Sea (SCS) are dominated by positive anomalies of diabatic heating associated with a stronger ISM and EASM, the anomalous westerly winds in the BOB and significantly anomalous easterly winds in the SCS, which carry more warm-wet air, induce a stronger than normal convergence and upward motion in the MRB. This results in much heavier rainy season precipitation in the MRB. Model runs with a linear baroclinic model confirm the observational analysis results.

Recently, much attention has been devoted to better understand the internal modes of variability of the climate system. This is particularly important in mid-latitude regions like the North-Atlantic, which is characterized by a large natural variability and is intrinsically difficult to predict. A suitable framework for studying the modes of variability of the atmospheric circulation is to look for recurrent patterns, commonly referred to as Weather Regimes. Each regime is characterized by a specific large-scale atmospheric circulation pattern, thus influencing regional weather and extremes over Europe. The focus of the present paper is the study of the Euro-Atlantic wintertime Weather Regimes in the climate models participating to the PRIMAVERA project. We analyse here the set of coupled historical simulations (hist-1950), which have been performed both at standard and increased resolution, following the HighresMIP protocol. The models’ performance in reproducing the observed Weather Regimes is assessed in terms of different metrics, focussing on systematic biases and on the impact of resolution. We also analyse the connection of the Weather Regimes with the Jet Stream latitude and blocking frequency over the North-Atlantic sector. We find that—for most models—the regime patterns are better represented in the higher resolution version, for all regimes but the NAO-. On the other side, no clear impact of resolution is seen on the regime frequency of occurrence and persistence. Also, for most models, the regimes tend to be more tightly clustered in the increased resolution simulations, more closely resembling the observed ones. However, the horizontal resolution is not the only factor determining the model performance, and we find some evidence that biases in the SSTs and mean geopotential field might also play a role.

This paper evaluates multi-decadal simulations of the UCLA version of Climate Forecast System version 2, in which the default Noah land surface model has been replaced with the Simplified Simple Biosphere Model version-2. To examine the influence of the atmosphere–ocean (AO) interaction on the variability, two different simulations were conducted: one with interactive ocean component, and the other constrained by the prescribed sea surface temperature. We evaluate the mean seasonal climatology of precipitation and temperature, along with the model’s ability to reproduce atmospheric variability at different scales over the globe, including extratropical modes of atmospheric variability, and long-term trends of global and hemispheric temperature and regional precipitation. Here, we particularly selected two monsoon regions, East Asia and West Africa, where the simulation of multi-decadal variations which has heretofore been a challenging task, to examine decadal variation of monsoon precipitation. In general, temperature anomaly trends were better captured than those of precipitation in both simulations. Results suggest that the AO interaction, represented as latent heat flux, contributes to improve reproducibility of global-wide climatology, extratropical modes of atmospheric variability, and variability in the multi-decadal climate simulation, as well as for inter-decadal variability of the East Asian summer monsoon.

We found an unintentional error in the calculation of monthly anomalies used in our previous paper (Arai and Kimoto 2008).

A comprehensive performance assessment of the empirical–statistical downscaling (ESD) technique named EPISODES is presented. Pertaining evaluation analyses consist of multifarious validation experiments as well as various comparisons of EPISODES’ projections with those of three RCMs and two ESD methods based on the same GCM scenarios driven by two distinct representative concentration pathways (RCPs). EPISODES combines the downscaling of GCM simulations with a follow-up production of synthetic local time series. EPISODES is a comparably simple, computationally rather inexpensive technique, providing multi-variable and multi-site data that are suitable for being merged in an ensemble of RCM projections. This allows (e.g. for different RCPs) the compilation of large multi member ensembles derived from various GCM simulations via both main downscaling strategies (ESD and RCMs). Evaluation experiments reveal satisfying degrees of compliance between various results generated by EPISODES and observations. The grid cell bias for yearly values, for instance, is mostly less than $$0.1\,^\circ$$ C for temperature and 10% for precipitation totals. Recorded temperature values and precipitation totals corresponding to their 1st and the 99th percentiles are well represented by EPISODES too. Comparisons of various climate change signals derived by EPISODES and other downscaling approaches, present high levels of agreement as well. Many more findings referring to evaluation experiments and climate change projections are to be found in the paper as well as throughout the “Appendix”.

Variational approaches to data assimilation, and weakly constrained four dimensional variation (WC-4DVar) in particular, are important in the geosciences but also in other communities (often under different names). The cost functions and the resulting optimal trajectories may have a probabilistic interpretation, for instance by linking data assimilation with Maximum Aposteriori (MAP) estimation. This is possible in particular if the unknown trajectory is modelled as the solution of a stochastic differential equation (SDE), as is increasingly the case in weather forecasting and climate modelling. In this case, the MAP estimator (or "most probable path" of the SDE) is obtained by minimising the Onsager--Machlup functional. Although this fact is well known, there seems to be some confusion in the literature, with the energy (or "least squares") functional sometimes been claimed to yield the most probable path. The first aim of this paper is to address this confusion and show that the energy functional does not, in general, provide the most probable path. The second aim is to discuss the implications in practice. Although the mentioned results pertain to stochastic models in continuous time, they do have consequences in practice where SDE's are approximated by discrete time schemes. It turns out that using an approximation to the SDE and calculating its most probable path does not necessarily yield a good approximation to the most probable path of the SDE proper. This suggest that even in discrete time, a version of the Onsager--Machlup functional should be used, rather than the energy functional, at least if the solution is to be interpreted as a MAP estimator., 19 pages, 3 Figures

The present paper uses the satellite era data from 1979 to 2015 to examine the relationship between El Nino-Southern Oscillation (ENSO) and tropical cyclones (TCs) in the western North Pacific (WNP) during the boreal summer from June to August. It is found that WNP TC variability is characterized by two major feature changes: (1) a significant reduction of the TC number since 1998 and (2) a stronger interannual relationship between ENSO and TCs since 1998. Results suggested that such changes are largely due to the synergy effects of a shifting ENSO and the Pacific climate regime shift. Since 1998 with a cool Pacific decadal oscillation phase switching from a warm phase, more La Nina and central Pacific (CP) El Nino events occur. The decreased low-level relative vorticity and increased vertical wind shear during 1998–2015 compared to 1979–1997 are responsible for the TC reduction. The stronger interannual relationship between ENSO and TCs since 1998 is closely associated the change of CP sea surface temperature. It enhances the associations of environmental factors including vertical wind shear and mid-level relative humidity with TCs and thus increases the interannual relationship between ENSO and TCs. These two feature changes also manifest in the mean TC genesis location, with a northwestward shift of the TC genesis location during 1998–2015 and an increased relation to El Nino Modoki index since 1998. This study has an important implication for TC outlooks in the WNP based on climate predictions and projections.

This paper investigates the optimal observational array for improving the initialization of El Nino-Southern Oscillation predictions by exploring the sensitive areas for target observations of two types of El Nino predictions. The sensitive areas are identified by calculating the optimally growing errors (OGEs) of the Zebiak–Cane model, as corrected by the optimal forcing vector that is determined by assimilating the observed sea surface temperature anomalies (SSTAs). It is found that although the OGEs have similar structures for different start months of predictions, the regions covered by much large errors for the SSTA component tend to locate at different zonal positions and depends on the start months. Furthermore, these regions are also in difference between two types of El Nino events. The regions covered by large errors of OGEs represent the sensitive areas for target observations. Considering the dependence of the sensitive areas on related El Nino types and the start months of predictions, the present study propose a quantitative frequency method to determine the sensitive areas for target observations associated with two types of El Nino predictions, which is expected to be applicable for both types of El Nino predictions with different start months. As a result, the sensitive areas that describe the array of target observations are presented with a reversal triangle-like shape locating in the eastern Pacific, specifically the area of 120°W–85°W, 0°S–11°S, and an extension to the west along the equator and then gathering at the 180° longitude and the western boundary. “Hindcast” experiments demonstrated that such observational array is very useful in distinguishing two types of El Nino and superior to the TAO/TRITON array. It is therefore suggested that the observational array provided in the present study is towards the optimal one and the original TAO/TRITON array should be further optimized when applied to predictions of the diversities of El Nino events.

Current climate models commonly overestimate precipitation over the Tibetan Plateau (TP), which limits our understanding of past and future water balance in the region. Identifying sources of such models’ wet bias is therefore crucial. The Himalayas is considered a major pathway of water vapor transport (WVT) towards the TP. Their steep terrain, together with associated small-scale processes, cannot be resolved by coarse-resolution models, which may result in excessive WVT towards the TP. This paper, therefore, investigated the resolution dependency of simulated WVT through the central Himalayas and its further impact on precipitation bias over the TP. According to a summer monsoon season of simulations conducted using the weather research forecasting (WRF) model with resolutions of 30, 10, and 2 km, the study found that finer resolutions (especially 2 km) diminish the positive precipitation bias over the TP. The higher-resolution simulations produce more precipitation over the southern Himalayan slopes and weaker WVT towards the TP, explaining the reduced wet bias. The decreased WVT is reflected mostly in the weakened wind speed, which is due to the fact that the high resolution can improve resolving orographic drag over a complex terrain and other processes associated with heterogeneous surface forcing. A significant difference was particularly found when the model resolution is changed from 30 to 10 km, suggesting that a resolution of approximately 10 km represents a good compromise between a more spatially detailed simulation of WVT and computational cost for a domain covering the whole TP.