High-resolution Atmospheric Model Research Articles

Sensitivity of the Brewer–Dobson Circulation and Polar Vortex Variability to Parameterized Nonorographic Gravity Wave Drag in a High-Resolution Atmospheric Model

The role of parameterized nonorographic gravity wave drag (NOGWD) and its seasonal interaction with the resolved wave drag in the stratosphere has been extensively studied in low-resolution (coarser than 1.9° × 2.5°) climate models but is comparatively unexplored in higher-resolution models. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 0.7° × 0.7° resolution, the wave drivers of the Brewer–Dobson circulation are diagnosed and the circulation sensitivity to the NOGW launch flux is explored. NOGWs are found to account for nearly 20% of the lower-stratospheric Southern Hemisphere (SH) polar cap downwelling and for less than 10% of the lower-stratospheric tropical upwelling and Northern Hemisphere (NH) polar cap downwelling. Despite these relatively small numbers, there are complex interactions between NOGWD and resolved wave drag, in both polar regions. Seasonal cycle analysis reveals a temporal offset in the resolved and parameterized wave interaction: the NOGWD response to altered source fluxes is largest in midwinter, while the resolved wave response is largest in the late winter and spring. This temporal offset is especially prominent in the SH. The impact of NOGWD on sudden stratospheric warming (SSW) life cycles and the final warming date in the SH is also investigated. An increase in NOGWD leads to an increase in SSW frequency, reduction in amplitude and persistence, and an earlier recovery of the stratopause following an SSW event. The SH final warming date is also brought forward when NOGWD is increased. Thus, NOGWD is still found to be a very important parameterization for stratospheric dynamics even in a high-resolution atmospheric model.

Relative humidity vertical profiling using lidar-based synergistic methods in the framework of the Hygra-CD campaign

Abstract. Accurate continuous measurements of relative humidity (RH) vertical profiles in the lower troposphere have become a significant scientific challenge. In recent years a synergy of various ground-based remote sensing instruments have been successfully used for RH vertical profiling, which has resulted in the improvement of spatial resolution and, in some cases, of the accuracy of the measurement. Some studies have also suggested the use of high-resolution model simulations as input datasets into RH vertical profiling techniques. In this paper we apply two synergetic methods for RH profiling, including the synergy of lidar with a microwave radiometer and high-resolution atmospheric modeling. The two methods are employed for RH retrieval between 100 and 6000 m with increased spatial resolution, based on datasets from the HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) campaign conducted in Athens, Greece from May to June 2014. RH profiles from synergetic methods are then compared with those retrieved using single instruments or as simulated by high-resolution models. Our proposed technique for RH profiling provides improved statistical agreement with reference to radiosoundings by 27 % when the lidar–radiometer (in comparison with radiometer measurements) approach is used and by 15 % when a lidar model is used (in comparison with WRF-model simulations). Mean uncertainty of RH due to temperature bias in RH profiling was ∼ 4.34 % for the lidar–radiometer and ∼ 1.22 % for the lidar–model methods. However, maximum uncertainty in RH retrievals due to temperature bias showed that lidar-model method is more reliable at heights greater than 2000 m. Overall, our results have demonstrated the capability of both combined methods for daytime measurements in heights between 100 and 6000 m when lidar–radiometer or lidar–WRF combined datasets are available.

Possible teleconnections between East and South Asian summer monsoon precipitation in projected future climate change

The present paper examined the teleconnections between two huge Asian summer monsoon components (South and East Asia) during three time slices in future: near-(2010–2039), mid-(2040–2069) and far-(2070–2100) futures under the RCP4.5 and RCP8.5 scenarios. For this purpose, a high-resolution atmospheric general circulation model is used and integrated at 40 km horizontal resolution. To get more insight into the relationships between the two Asian monsoon components, we have studied the spatial displaying correlation coefficients (CCs) pattern of precipitation over the entire Asian monsoon region with that of South Asia and three regions of East Asia (North China, Korea–Japan and Southern China) separately during the same three time slices. The possible factors responsible for these teleconnections are explored by using mean sea level pressure (MSLP) and wind fields at 850 hPa. The CC pattern of precipitation over South Asia shows an in-phase relationship with North China and an out-of-phase relationship with Korea–Japan, while precipitation variations over Korea–Japan and Southern China exhibit an out-of-phase relationship with South Asia. The CCs analysis between the two Asian blocks during different time slices shows the strongest CCs during the near and far future with the RCP8.5 scenario. The CC pattern of precipitation over Korea–Japan and Southern China with the wind (at 850 hPa) and MSLP fields indicate that the major parts of the moisture over Korea–Japan gets transported from the west Pacific along the western limb of NPSH, while the moisture over Southern China comes from the Bay of Bengal and South China Seas for good monsoon activity.

Adaptation to climate change of the French wine industry: a systemic approach – Main outcomes of the project LACCAVE

Taking into account the major economical role and specificities of the French wine industry, adaptation to climate change is a very challenging issue. In 2011, 23 research teams launched a systemic and multidisciplinary program to analyze the impacts from the vine to the region, to define adaptation strategies combining technical, spatial and organizational options and to evaluate the perception by the actors and consumers of climate change issues. Thermal variability was studied at local scale to develop high resolution atmospheric models which better simulate future climate trends. Impacts on growth/developmental conditions and vine responses were estimated from the calculation of eco-climatic indices and a combination of functional models. Genetic and physiological bases of grapevine adaptation to high temperature and drought were analyzed. Improving oenological and cultural practices as well as plant material innovation have been investigated as major technical adaptations. How these options could be implemented at the plot level was examined to elaborate decision tools. Multi-agent modelling was developed for this purpose. Surveys were performed to evaluate the perception of the main actors regarding climate change and their level of acceptability towards technical changes. Consumer acceptability of new types of wines was also investigated with an experimental economy approach. Finally a foresight exercise was conducted to design four potential adaptation strategies: conservative, innovative, nomad and liberal. Outcomes of this exercise are now used as a tool for wine industry members to develop their own strategic plan for adaptation.

Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities

We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3) in three US cities for summers during 1996–2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O3. We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3.

Impact of Intraseasonal Oscillations on the Tropical Cyclone Activity Over the Gulf of Mexico and Western Caribbean Sea in GFDL HiRAM

AbstractThe tropical cyclones (TCs) that form over the warm waters in the Gulf of Mexico region pose a major threat to the surrounding coastal communities. Skillful subseasonal prediction of TC activity is important for early preparedness and reducing the TC damage in this region. In this study, we evaluate the performance of a 25 km resolution Geophysical Fluid Dynamics Laboratory (GFDL) High Resolution Atmospheric Model (HiRAM) in simulating the modulation of the TC activity in the Gulf of Mexico and western Caribbean Sea by the intraseasonal oscillation (ISO) based on multiyear retrospective seasonal predictions. We demonstrate that the HiRAM faithfully captures the observed influence of ISO on TC activity over the region of interest, including the formation of tropical storms and (major) hurricanes, as well as the landfalling storms. This is likely because of the realistic representation of the large‐scale anomalies associated with boreal summer ISO over Northeast Pacific in HiRAM, especially the enhanced (reduced) moisture throughout the troposphere during the convectively enhanced (suppressed) phase of ISO. The reasonable performance of HiRAM suggests its potential for the subseasonal prediction of regional TC risk.

Process-Oriented Diagnosis of Tropical Cyclones in High-Resolution GCMs

This study proposes a set of process-oriented diagnostics with the aim of understanding how model physics and numerics control the representation of tropical cyclones (TCs), especially their intensity distribution, in GCMs. Three simulations are made using two 50-km GCMs developed at NOAA’s Geophysical Fluid Dynamics Laboratory. The two models are forced with the observed sea surface temperature [Atmospheric Model version 2.5 (AM2.5) and High Resolution Atmospheric Model (HiRAM)], and in the third simulation, the AM2.5 model is coupled to an ocean GCM [Forecast-Oriented Low Ocean Resolution (FLOR)]. The frequency distributions of maximum near-surface wind near TC centers show that HiRAM tends to develop stronger TCs than the other models do. Large-scale environmental parameters, such as potential intensity, do not explain the differences between HiRAM and the other models. It is found that HiRAM produces a greater amount of precipitation near the TC center, suggesting that associated greater diabatic heating enables TCs to become stronger in HiRAM. HiRAM also shows a greater contrast in relative humidity and surface latent heat flux between the inner and outer regions of TCs. Various fields are composited on precipitation percentiles to reveal the essential character of the interaction among convection, moisture, and surface heat flux. Results show that the moisture sensitivity of convection is higher in HiRAM than in the other model simulations. HiRAM also exhibits a stronger feedback from surface latent heat flux to convection via near-surface wind speed in heavy rain-rate regimes. The results emphasize that the moisture–convection coupling and the surface heat flux feedback are critical processes that affect the intensity of TCs in GCMs.

Spatiotemporal patterns of the fossil-fuel CO<sub>2</sub> signal in central Europe: results from a high-resolution atmospheric transport model

Abstract. The emission of CO2 from the burning of fossil fuel is a prime determinant of variations in atmospheric CO2. Here, we simulate this fossil-fuel signal together with the natural and background components with a regional high-resolution atmospheric transport model for central and southern Europe considering separately the emissions from different sectors and countries on the basis of emission inventories and hourly emission time functions. The simulated variations in atmospheric CO2 agree very well with observation-based estimates, although the observed variance is slightly underestimated, particularly for the fossil-fuel component. Despite relatively rapid atmospheric mixing, the simulated fossil-fuel signal reveals distinct annual mean structures deep into the troposphere, reflecting the spatially dense aggregation of most emissions. The fossil-fuel signal accounts for more than half of the total (fossil fuel + biospheric + background) temporal variations in atmospheric CO2 in most areas of northern and western central Europe, with the largest variations occurring on diurnal timescales owing to the combination of diurnal variations in emissions and atmospheric mixing and transport out of the surface layer. The covariance of the fossil-fuel emissions and atmospheric transport on diurnal timescales leads to a diurnal fossil-fuel rectifier effect of up to 9 ppm compared to a case with time-constant emissions. The spatial pattern of CO2 from the different sectors largely reflects the distribution and relative magnitude of the corresponding emissions, with power plant emissions leaving the most distinguished mark. An exception is southern and western Europe, where the emissions from the transportation sector dominate the fossil-fuel signal. Most of the fossil-fuel CO2 remains within the country responsible for the emission, although in smaller countries up to 80 % of the fossil-fuel signal can come from abroad. A fossil-fuel emission reduction of 30 % is clearly detectable for a surface-based observing system for atmospheric CO2, while it is beyond the edge of detectability for the current generation of satellites with the exception of a few hotspot sites. Changes in variability in atmospheric CO2 might open an additional door for the monitoring and verification of changes in fossil-fuel emissions, primarily for surface-based systems.

RCP8.5-Based Future Flood Hazard Analysis for the Lower Mekong River Basin

Climatic variations caused by the excessive emission of greenhouse gases are likely to change the patterns of precipitation, runoff processes, and water storage of river basins. Various studies have been conducted based on precipitation outputs of the global scale climatic models under different emission scenarios. However, there is a limitation in regional- and local-scale hydrological analysis on extreme floods with the combined application of high-resolution atmospheric general circulation models’ (AGCM) outputs and physically-based hydrological models (PBHM). This study has taken an effort to overcome that limitation in hydrological analysis. The present and future precipitation, river runoff, and inundation distributions for the Lower Mekong Basin (LMB) were analyzed to understand hydrological changes in the LMB under the RCP8.5 scenario. The downstream area beyond the Kratie gauging station, located in the Cambodia and Vietnam flood plains was considered as the LMB in this study. The bias-corrected precipitation outputs of the Japan Meteorological Research Institute atmospheric general circulation model (MRI-AGCM3.2S) with 20 km horizontal resolution were utilized as the precipitation inputs for basin-scale hydrological simulations. The present climate (1979–2003) was represented by the AMIP-type simulations while the future (2075–2099) climatic conditions were obtained based on the RCP8.5 greenhouse gas scenario. The entire hydrological system of the Mekong basin was modelled by the block-wise TOPMODEL (BTOP) hydrological model with 20 km resolution, while the LMB area was modelled by the rainfall-runoff-inundation (RRI) model with 2 km resolution, specifically to analyze floods under the aforementioned climatic conditions. The comparison of present and future river runoffs, inundation distributions and inundation volume changes were the outcomes of the study, which can be supportive information for the LMB flood management, water policy, and water resources development.

Improved Performance of High-Resolution Atmospheric Models in Simulating the East Asian Summer Monsoon Rain Belt

Simulating the East Asian summer monsoon (EASM) rain belt has been proven challenging for climate models. In this study, the impacts of high resolution to the simulation of spatial distributions and rainfall intensity of the EASM rain belt are revealed based on Atmospheric Model Intercomparison Project (AMIP) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. A set of sensitivity experiments is further performed to eliminate the potential influences of differences among CMIP5 models. The results show that the high-resolution models improve the intensity and the spatial pattern of the EASM rainfall compared to the low-resolution models, further valid in the sensitivity experiments. The diagnosis of moist static energy (MSE) balance and moisture budgets is further performed to understand the mechanisms underlying the enhancements. Both analyses indicate that the improved EASM rainfall benefits from the intensified meridional convergence along the EASM rain belt simulated by the high-resolution models. In addition, such convergence is mainly contributed by intensified stationary meridional eddy northerly flows over the central northern areas of China and southerly flows over the south of Japan due to increased model resolution, which is robust in the sensitivity experiments. Further analysis indicates that the stationary meridional eddy flow changes in high-resolution simulations are related to the barotropic Rossby wave downstream of the Tibetan Plateau resulting from increased resolution.

Atmospheric observations show accurate reporting and little growth in India\u2019s methane emissions

Changes in tropical wetland, ruminant or rice emissions are thought to have played a role in recent variations in atmospheric methane (CH4) concentrations. India has the world’s largest ruminant population and produces ~ 20% of the world’s rice. Therefore, changes in these sources could have significant implications for global warming. Here, we infer India’s CH4 emissions for the period 2010–2015 using a combination of satellite, surface and aircraft data. We apply a high-resolution atmospheric transport model to simulate data from these platforms to infer fluxes at sub-national scales and to quantify changes in rice emissions. We find that average emissions over this period are 22.0 (19.6–24.3) Tg yr−1, which is consistent with the emissions reported by India to the United Framework Convention on Climate Change. Annual emissions have not changed significantly (0.2 ± 0.7 Tg yr−1) between 2010 and 2015, suggesting that major CH4 sources did not change appreciably. These findings are in contrast to another major economy, China, which has shown significant growth in recent years due to increasing fossil fuel emissions. However, the trend in a global emission inventory has been overestimated for China due to incorrect rate of fossil fuel growth. Here, we find growth has been overestimated in India but likely due to ruminant and waste sectors.

Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling

Methane is an important greenhouse gas due to its high warming potential. While quantifying anthropogenic methane emissions is important for evaluation measures applied for climate change mitigation, large emission uncertainties still exist for many source categories. To evaluate anthropogenic methane emission inventory in various regions over the globe, we extract emission signatures from column-average methane observations (XCH4) by GOSAT (Greenhouse gases Observing SATellite) satellite using high-resolution atmospheric transport model simulations. XCH4 abundance due to anthropogenic emissions is estimated as the difference between polluted observations from surrounding cleaner observations. Here, reduction of observation error, which is large compared to local abundance, is achieved by binning the observations over large region according to model-simulated enhancements. We found that the local enhancements observed by GOSAT scale linearly with inventory based simulations of XCH4 for the globe, East Asia and North America. Weighted linear regression of observation derived and inventory-based XCH4 anomalies was carried out to find a scale factor by which the inventory agrees with the observations. Over East Asia, the observed enhancements are 30% lower than suggested by emission inventory, implying a potential overestimation in the inventory. On the contrary, in North America, the observations are approximately 28% higher than model predictions, indicating an underestimation in emission inventory. Our results concur with several recent studies using other analysis methodologies, and thus confirm that satellite observations provide an additional tool for bottom-up emission inventory verification.

Distributions of Tropical Precipitation Cluster Power and Their Changes under Global Warming. Part I: Observational Baseline and Comparison to a High-Resolution Atmospheric Model

Abstract The total amount of precipitation integrated across a precipitation feature (contiguous precipitating grid cells exceeding a minimum rain rate) is a useful measure of the aggregate size of the disturbance, expressed as the rate of water mass lost or latent heat released (i.e., the power of the disturbance). The probability distribution of cluster power is examined over the tropics using Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite-retrieved rain rates and global climate model output. Observed distributions are scale-free from the smallest clusters up to a cutoff scale at high cluster power, after which the probability drops rapidly. After establishing an observational baseline, precipitation from the High Resolution Atmospheric Model (HiRAM) at two horizontal grid spacings (roughly 0.5° and 0.25°) is compared. When low rain rates are excluded by choosing a minimum rain-rate threshold in defining clusters, the model accurately reproduces observed cluster power statistics at both resolutions. Middle and end-of-century cluster power distributions are investigated in HiRAM in simulations with prescribed sea surface temperatures and greenhouse gas concentrations from a “business as usual” global warming scenario. The probability of high cluster power events increases strongly by end of century, exceeding a factor of 10 for the highest power events for which statistics can be computed. Clausius–Clapeyron scaling accounts for only a fraction of the increased probability of high cluster power events.

Distributions of Tropical Precipitation Cluster Power and Their Changes under Global Warming. Part II: Long-Term Time Dependence in Coupled Model Intercomparison Project Phase 5 Models

Abstract Distributions of precipitation cluster power (latent heat release rate integrated over contiguous precipitating pixels) are examined in 1°–2°-resolution members of phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate model ensemble. These approximately reproduce the power-law range and large event cutoff seen in observations and the High Resolution Atmospheric Model (HiRAM) at 0.25°–0.5° in Part I. Under the representative concentration pathway 8.5 (RCP8.5) global warming scenario, the change in the probability of the most intense storm clusters appears in all models and is consistent with HiRAM output, increasing by up to an order of magnitude relative to historical climate. For the three models in the ensemble with continuous time series of high-resolution output, there is substantial variability on when these probability increases for the most powerful storm clusters become detectable, ranging from detectable within the observational period to statistically significant trends emerging only after 2050. A similar analysis of National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) AMIP-II reanalysis and Special Sensor Microwave Imager and Imager/Sounder (SSM/I and SSMIS) rain-rate retrievals in the recent observational record does not yield reliable evidence of trends in high power cluster probabilities at this time. However, the results suggest that maintaining a consistent set of overlapping satellite instrumentation with improvements to SSM/I–SSMIS rain-rate retrieval intercalibrations would be useful for detecting trends in this important tail behavior within the next couple of decades.

Atmospheric Rivers over the Northwestern Pacific: Climatology and Interannual Variability

AbstractAtmospheric rivers (ARs), conduits of intense water vapor transport in the midlatitudes, are critically important for water resources and heavy rainfall events over the west coast of North America, Europe, and Africa. ARs are also frequently observed over the northwestern Pacific (NWP) during boreal summer but have not been studied comprehensively. Here the climatology, seasonal variation, interannual variability, and predictability of NWP ARs (NWPARs) are examined by using a large ensemble, high-resolution atmospheric general circulation model (AGCM) simulation and a global atmospheric reanalysis. The AGCM captures general characteristics of climatology and variability compared to the reanalysis, suggesting a strong sea surface temperature (SST) effect on NWPARs. The summertime NWPAR occurrences are tightly related to El Niño–Southern Oscillation (ENSO) in the preceding winter through Indo–western Pacific Ocean capacitor (IPOC) effects. An enhanced East Asian summer monsoon and a low-level anticyclonic anomaly over the tropical western North Pacific in the post–El Niño summer reinforce low-level water vapor transport from the tropics with increased occurrence of NWPARs. The strong coupling with ENSO and IPOC indicates a high predictability of anomalous summertime NWPAR activity.

New approach to incorporating the impacts of non-hydrostatic perturbations in atmospheric models

AbstractHow to properly consider the impacts of non-hydrostatic perturbations is one of the challenging issues in developing non-hydrostatic dynamics solvers (NHDSs) for high-resolution atmospheric models. To overcome the drawbacks of current approaches to tackling this issue, this study analyzed the differences between hydrostatic dynamics solvers (HDSs) and their non-hydrostatic counterparts. The analysis then motivated a flexible approach to adjusting existing hydrostatic atmospheric models, especially those adopted in climate simulations for the impacts of non-hydrostatic perturbations. In this approach, the impacts of non-hydrostatic perturbations, reflecting the differences between HDSs and NHDSs, were encapsulated into a single term in the vertical momentum equation for the atmosphere. At each time step, this term was estimated by a separate sub-model, and then it was used to adjust the dynamics of the atmosphere. The adjustment was optional, and could be turned on and off flexibly by utilizing dif...

Atmospheric conditions affecting seeing at St. Catherine: Estimation of operational time for NRIAG new telescope

The study of the prevailing atmospheric conditions is an essential part of any site testing for a new telescope establishment. In this article, the meteorological parameters that affect the astronomical seeing at St. Catherine region, where a two candidate sites are located, are studied based on the available climate data. The complex topographical features of the region cause some differences between the weather at the nearest meteorological station and that at the candidate sites. This issue is illustrated through high resolution atmospheric modeling for short period (six days) as a case study. Finally, a preliminary estimation of operational hours for the telescope at the candidate sites is calculated.

Projection in snowfall characteristics over the European Alps and its sensitivity to the SST changes: results from a 50 km resolution AGCM

AbstractThe end‐of‐century projection of the snowfall characteristics over the Alps region is studied using the 50‐km resolution atmospheric global climate model, HiRAM (high‐resolution atmospheric model). The model is forced by three different patterns of projections in the sea surface temperature (SST) in order to assess the sensitivity of snowfall characteristics to theses patterns. It is found that the mean snowfall intensity and frequency is poorly affected by the differences in SST forcing. However, the projections of heavy snowfall events strongly depend on the SST scenario. The changes in temperature and frequency of precipitation and freezing days over the Alps were investigated. We found that these variables did not exhibit a clear dependence to the SST scenario and could not explain the differences observed in snowfall projections. Changes in the moisture transport from the Atlantic Ocean to Europe were found significantly different between each scenario and are assumed to be the main factor affecting the projections of snowfalls, by providing more or less moisture supply.

The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions

<p>Grapevines are highly sensitive to environmental conditions, with variability in weather and climate (particularly temperature) having a significant influence on wine quality, quantity and style. Improved knowledge of spatial and temporal variations in climate and their impact on grapevine response allows better decision-making to help maintain a sustainable wine industry in the context of medium to long term climate change. This paper describes recent research into the application of mesoscale weather and climate models that aims to improve our understanding of climate variability at high spatial (1 km and less) and temporal (hourly) resolution within vineyard regions of varying terrain complexity. The Weather Research and Forecasting (WRF) model has been used to simulate the weather and climate in the complex terrain of the Marlborough region of New Zealand. The performance of the WRF model in reproducing the temperature variability across vineyard regions is assessed through comparison with automatic weather stations. Coupling the atmospheric model with bioclimatic indices and phenological models (e.g. Huglin, cool nights, Grapevine Flowering Véraison model) also provides useful insights into grapevine response to spatial variability of climate during the growing season, as well as assessment of spatial variability in the optimal climate conditions for specific grape varieties.</p>

The application of high-resolution atmospheric modelling to weather and climate variability in vineyard regions

Grapevines are highly sensitive to environmental conditions, with variability in weather and climate (particularly temperature) having a significant influence on wine quality, quantity and style. Improved knowledge of spatial and temporal variations in climate and their impact on grapevine response allows better decision-making to help maintain a sustainable wine industry in the context of medium to long term climate change. This paper describes recent research into the application of mesoscale weather and climate models that aims to improve our understanding of climate variability at high spatial (1 km and less) and temporal (hourly) resolution within vineyard regions of varying terrain complexity. The Weather Research and Forecasting (WRF) model has been used to simulate the weather and climate in the complex terrain of the Marlborough region of New Zealand. The performance of the WRF model in reproducing the temperature variability across vineyard regions is assessed through comparison with automatic weather stations. Coupling the atmospheric model with bioclimatic indices and phenological models (e.g. Huglin, cool nights, Grapevine Flowering Véraison model) also provides useful insights into grapevine response to spatial variability of climate during the growing season, as well as assessment of spatial variability in the optimal climate conditions for specific grape varieties.