A regional climate model for the western United States

A numerical approach to modeling climate on a regional scale is developed whereby large-scale weather systems are simulated with a global climate model (GCM) and the GCM output is used to provide the boundary conditions needed for high-resolution mesoscale model simulations over the region of interest. In our example, we use the National Center for Atmospheric Research (NCAR) community climate model (CCM1) and the Pennsylvania State University (PSU)/NCAR Mesoscale Model version 4 (MM4) to apply this approach over the western United States (U.S.). The topography, as resolved by the 500-km mesh of the CCM1, is necessarily highly distorted, but with the 60-km mesh of the MM4 the major mountain ranges are distinguished. To obtain adequate and consistent representations of surface climate, we use the same radiation and land surface treatments in both models, the latter being the recently developed Biosphere-Atmosphere Transfer Scheme (BATS). Our analysis emphasizes the simulation at four CCM1 points surrounding Yucca Mountain, NV, because of the need to determine its climatology prior to certification as a high-level nuclear waste repository.We simulate global climate for three years with CCM1/BATS and describe the resulting January surface climatology over the western U.S. The details of the precipitation patterns are unrealistic because of the smooth topography. Selecting five January CCM1 storms that occur over the western U.S. with a total duration of 20 days for simulation with the MM4, we demonstrate that the mesoscale model provides much improved wintertime precipitation patterns. The storms in MM4 are individually much more realistic than those in CCM1. A simple averaging procedure that infers a mean January rainfall climatology calculated from the 20 days of MM4 simulation is much closer to the observed than is the CCM1 climatology. The soil moisture and subsurface drainage simulated over 3–5 day integration periods of MM4, however, remain strongly dependent on the initial CCM1 soil moisture and thus are less realistic than the rainfall. Adequate simulation of surface soil water may require integrations of the mesoscale model over time periods.The National Center for Atmospheric Research is sponsored by the National Science Foundation. of up to several months or longer.     

Applications of the cyclic spectral analysis to the surface temperature fluctuations in a stochastic climate model and a GCM simulation

Abstract

In this paper, we introduce the cyclostationary processes into climate analysis and undertake a systematic study of the cyclic spectra of surface temperature fluctuations. The technique is adapted from cyclostationarity theory in signal processing. To demonstrate the usefulness of this technique, a very simple cyclostationary stochastic climate model is constructed. Our results show that the seasonal cycle strongly modulates the amplitudes of the covariance and the spectrum. The technique was also applied to the surface temperature fluctuations in a fifteen‐year seasonal run of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2, R15) using a zonally symmetric all‐land surface as the lower boundary. The results indicate that intraseasonal oscillations localized according to time of year are still present even after the surface temperature fields have been normalized using the commonly used procedure. Both examples suggest that the “annual cycle” cannot be “removed” by simply using a normalization procedure. The climate is not as completely represented when modelled as stationary processes.     

Sensitivity of ecosystem models to the spatial resolution of the NCAR Community Climate Model CCM2

 This study evaluates the sensitivity of ecosystem models to changes in the horizontal resolution of version 2 of the National Centre for Atmospheric Research Community Climate Model (CCM2). A previous study has shown that the distributions of natural ecosystems predicted by vegetation models using coarse resolution present-day climate simulations are poorly simulated. It is usually assumed that increasing the spatial resolution of general circulation models (GCMs) will improve the simulation of climate, and hence will increase our level of confidence in the use of GCM output for impacts studies. The principal goals of this study is to investigate this hypothesis and to identify which biomes are more affected by the changes in spatial resolution of the forcing climate. The ecosystem models used are the BIOME-1 model and a version of the Holdridge scheme. The climate simulations come from a set of experiments in which CCM2 was run with increasing horizontal resolutions. The biome distributions predicted using CCM2 climates are compared against biome distributions predicted using observed climate datasets. Results show that increasing the resolution of CCM2 produces a significant improvement of the global-scale vegetation prediction, indicating that a higher level of confidence can be vested in the global-scale prediction of natural ecosystems using medium and high resolution GCMs. However, not all biomes are equally affected by the increased spatial resolution, and although certain biome distributions are improved (e.g. hot desert, tropical seasonal forest), others remain globally poorly predicted even at high resolution (e.g. grasses and xerophytic woods). In addition, these results show that some climatic biases are enhanced with increasing resolution (e.g. in mountain ranges), resulting in the inadequate prediction of biomes. Received: 4 March 1997 / Accepted: 10 December 1997     

Vegetation patterns in South America associated with rising CO2: uncertainties related to sea surface temperatures

Spatially precise forecasts of the impacts of climate change on the distribution of major vegetation types are essential for the implementation of effective conservation and land use policy. However, existing studies frequently omit major sources of climate variability that can significantly increase the uncertainty of projections. In this study we demonstrate how different predictions for sea surface temperature (SST) for the first half of the twenty-first century increase the uncertainty associated with forecasts of the future distribution of major ecosystems in South America. This is demonstrated through a numerical experiment using a coupled climate–vegetation model (CCM3-IBIS) for IPCC emission scenario A2 that incorporates the SST data from ten different models. The study reveals an increasing uncertainty in the ability to forecast future vegetation patterns, such that by 2050 the simulation is unable to robustly forecast the vegetation cover in an area equivalent to 28 % in South America (5?×?10⁶ km²). The future of the central and northeastern regions of Brazil is especially uncertain, with outcomes, ranging from savanna, and open shrubland to grassland. Recognizing and managing such uncertainty should be a priority for decision makers.     

High-resolution simulations of global climate, part 2: effects of increased greenhouse cases

We report results from the highest-resolution simulations of global warming yet performed with an atmospheric general circulation model. We compare the climatic response to increased greenhouse gases of the National Center for Atmospheric Research (NCAR) climate model, CCM3, at T42 and T170 resolutions (horizontal grid spacing of 300 and 75 km respectively). All simulations use prescribed sea surface temperatures (SST). Simulations of the climate of 2100 ad use SSTs based on those from NCAR coupled model, Climate System Model (CSM). We find that the global climate sensitivity and large-scale patterns of climate change are similar at T42 and T170. However, there are important regional scale differences that arise due to better representation of topography and other factors at high resolution. Caution should be exercised in interpreting specific features in our results both because we have performed climate simulations using a single atmospheric general circulation model and because we used with prescribed sea surface temperatures rather than interactive ocean and sea-ice models.     

Investing in low-carbon transitions: energy finance as an adaptive market

The amount of capital required to transition energy systems to low-carbon futures is very large, yet analysis of energy systems change has been curiously quiet on the role of capital markets in financing energy transitions. This is surprising given the huge role finance and investment must play in facilitating transformative change. We argue this has been due to a lack of suitable theory to supplant neoclassical notions of capital markets and innovation finance. This research draws on the notion from Planetary economics: Energy, climate change and the three domains of sustainable development, by Grubb and colleagues, that planetary economics is defined by three ‘domains’, which describe behavioural, neoclassical, and evolutionary aspects of energy and climate policy analysis. We identify first- and second-domain theories of finance that are well established, but argue that third-domain approaches, relating to evolutionary systems change, have lacked a compatible theory of capital markets. Based on an analysis of electricity market reform and renewable energy finance in the UK, the ‘adaptive market hypothesis' is presented as a suitable framework with which to analyse energy systems finance. Armed with an understanding of financial markets as adaptive, scholars and policy makers can ask new questions about the role of capital markets in energy systems transitions.

Policy relevance

This article explores the role of financial markets in capitalising low-carbon energy systems and long-term change. The authors demonstrate that much energy and climate policy assumes financial markets are efficient, meaning they will reliably capitalise low-carbon transitions if a rational return is created by subsidy regimes or other market mechanisms. The authors show that the market for renewable energy finance does not conform to the efficient markets hypothesis, and is more in line with an ‘adaptive’ markets understanding. Climate and energy policy makers that design policy, strategy, and regulation on the assumption of efficient financial markets will not pay attention to structural and behavioural constraints on investment; they risk falling short of the investment levels needed for long-term systems change. In short, by thinking of financial markets as adaptive, the range of policy responses to enable low-carbon investment can be much broader.     

SIMULATION OF REGIONAL CLIMATE OVER CHINA WITH CHINESE REGIONAL CLIMATE MODEL

In this paper,we present the results simulated with the Chinese regional climate model nestedin NCAR CCM1 GCM through one-way nesting approach.The model has been run for 14 months.The NCAR CCM1(1992)is at rhomboidal truncation(R15),while the horizontal resolution ofthe Chinese regional climate model is 100 km.It is found that the Chinese regional climate modelhas some advantages in simulating the surface air temperature and precipitation over the generalclimate model,because of the improved land surface parameterization.     

Developing a model of climate change behavior among adolescents

Research on adolescent climate change perceptions has uncovered key insights about how knowledge, concern, and hope might relate to behavior and the potential for educational interventions to influence these factors. However, few of these studies have employed treatment/control designs that might address causality and none have addressed how these factors might interact to influence behavior. We developed a model of behavior change where a climate education treatment impacted knowledge, knowledge impacted hope and concern, and hope and concern together impacted behavior. We empirically tested the utility of this model and the causal relationships within it using a pre/post, treatment/control evaluation of climate education among adolescents in North Carolina, USA (n?=?1041). We found support for a causal relationship between the treatment and gains in knowledge, but not between treatment and behavior. However, we did find support for a path model in which climate change knowledge positively relates to increased climate change concern and hope, and increases in concern and hope predict changes in pro-environmental behavior. Low SES was related to smaller gains in knowledge, concern, and behavior. Our results contribute to a theoretical understanding of climate change behaviors among adolescents and suggest that climate education aiming to change behavior should focus on building hope and concern.     

Physically based evaluation of climate models over the Iberian Peninsula

A novel approach is proposed for evaluating regional climate models based on the comparison of empirical relationships among model outcome variables. The approach is actually a quantitative adaptation of the method for evaluating global climate models proposed by Betts (Bull Am Meteorol Soc 85:1673–1688, 2004). Three selected relationships among different magnitudes involved in water and energy land surface budgets are firstly established using daily re-analysis data. The selected relationships are obtained for an area encompassing two river basins in the southern Iberian Peninsula corresponding to 2 months, representative of dry and wet seasons. The same corresponding relations are also computed for each of the thirteen regional simulations of the ENSEMBLES project over the same area. The usage of a metric based on the Hellinger coefficient allows a quantitative estimation of how well models are performing in simulating the relations among surface magnitudes. Finally, a series of six rankings of the thirteen regional climate models participating in the ENSEMBLES project is obtained based on their ability to simulate such surface processes.     

Comparison of the climate simulated by the CCM3 coupled to two different land-surface models

We present results from a coupled atmosphere-biosphere model CCM3/IBIS (the Community Climate Model coupled to the Integrated BIosphere Simulator), which is designed to study the dynamic interactions between climate and vegetation and the global carbon cycle. We analyze the climate simulated by CCM3/IBIS with fixed vegetation conditions and we compare it to the climate simulated by the standard CCM3, which includes the LSM (land surface model) land-surface package. Important differences between the two models include simple parametrizations of lakes, wetlands and crops in CCM3/LSM not taken into account in CCM3/IBIS. CCM3/IBIS and CCM3/LSM share common biases (compared to observations) in the temperature field in boreal winter and in the precipitation field annually, making the atmospheric model the most probable cause of those biases. The models differ in the temperature field and surface energy balance in the Sahara annually and in the mid-to high latitudes from spring through fall. CCM3/IBIS simulates global annual air temperatures that are on average 1.7 °C higher than CCM3/LSM and 0.5 °C higher than the observed climatology. Differences in albedo and/or snow parametrization explain most of the Sahara and high-latitude temperature disagreement. Our sensitivity study with CCM3/LSM shows that the presence of lakes and wetlands in CCM3/LSM can account for about half of the difference in temperature in summer over the lake and wetland regions of the mid-latitudes. A second sensitivity study shows that higher surface roughness length in CCM3/IBIS can also explain part of the difference in summer surface temperature in the mid-latitudes. Surface roughness length affects the surface temperature through a feedback mechanism linking surface wind speed, planetary boundary layer height, low level cloudiness and radiation     

The Challenges of Modeling Climate Variability and Change in West Africa

This paper overviews observations and examines modeling issues associated with the mean state, climate variability and climate change in West Africa. The Tropical Rain Measuring Mission (TRMM) satellite allows for the first time estimates of Unconditional, Convective and Stratiform rain rates in West Africa. The 1998 estimated TRMM rates are compared to long-term observed rain rates and a merged rain data set (CMAP) during 1998. Further, the TRMM estimates are compared to the simulated rain rates from the Community Climate Model Version 3.6. The TRMM Precipitation Radar rain estimates are generally lower than either the long-term observations or the CMAP rates during 1998. Moreover, the TRMM rain estimates show a significant fraction of the total rain (convective + stratiform) is characterized as stratiform rain (30–40%). The CCM3 simulates primarily convective rain and negligible amounts of non-convective rain for West Africa. Furthermore, the TRMM high-resolution rain patterns strongly imply that rain in West Africa occurs on mesoscales in association with mesoscale convective systems (squall lines, mesoscale convective complexes and non-squall tropical clusters). We demonstrate this by briefly examining two mesoscale convective systems during May 1998 with METEOSAT data. Regional climate models may offer the best solution to understanding climate change in West Africa because of their ability to capture mesoscale systems and better their representation of orographic features. Adequate boundary conditions from Global Climate Models are still necessary for regional climate model simulations to successfully reproduce mean climate conditions and provide understanding with respect to future climate change. Observations in West Africa should be maintained or increased for monitoring climate variability and possibility of climate change in West Africa, proper initialization of numerical weather prediction models and the validation of climate models.     

Decreasing predictability as a precursor indicator for abrupt climate change

Abrupt climate change has an important impact on sustainable economic and social development, as well as ecosystem. However, it is very difficult to predict abrupt climate changes because the climate system is a complex and nonlinear system. In the present paper, the nonlinear local Lyapunov exponent (NLLE) is proposed as a new early warning signal for an abrupt climate change. The performance of NLLE as an early warning signal is first verified by those simulated abrupt changes based on four folding models. That is, NLLE in all experiments showed an almost monotonous increasing trend as a dynamic system approached its tipping point. For a well-studied abrupt climate change in North Pacific in 1976/1977, it is also found that NLLE shows an almost monotonous increasing trend since 1970 which give up to 6 years warning before the abrupt climate change. The limit of the predictability for a nonlinear dynamic system can be quantitatively estimated by NLLE, and lager NLLE of the system means less predictability. Therefore, the decreasing predictability may be an effective precursor indicator for abrupt climate change.

     

Large-scale climate variability and its effects on mean temperature and flowering time of Prunus and Betula in Denmark

Summary Large-scale climate variability largely affects average climatic conditions and therefore is likely to influence the phenology of plants. In NW-Europe, the North Atlantic Oscillation (NAO) particularly influences winter climate and, through climate interactions on plants, flowering time of all tree species. In Denmark, like in many other NW-European countries, flowering of most tree species has become earlier since the end of the 1980’s. To quantify a possible relation between NAO and flowering time of tree species, two sources of phenological information from the Copenhagen area (Denmark) were analysed, i.e. pollen counts of the genus Betula and observed first bloom dates of Prunus avium. The Winter NAO explained 29 and 37% of the variation of monthly mean temperature for February and March, respectively. The influence of temperature on flowering time was up to 56% to 60% for the February–April mean. A direct correlation of Winter NAO-index and flowering time also revealed a clear relation but the time of influence was earlier (December to February). This was shown to be the likely result of a combination of direct and time-lagged effects of the NAO on air and sea surface temperature. The NAO signal is apparently stored in the North Sea and then influences temperature east up to the Baltic States. It is shown that Denmark is right in the centre of direct and time-lagged effects of the NAO. This offers the possibility of using the NAO-index for predicting flowering time of Prunus avium. The beginning of pollen flow appears to be influenced too much by short-term perturbations of the climate system decreasing the value of the NAO-index for prediction. However, it indicates a close relationship between natural climate variability, measured by the NAO index, and flowering time of tree species for Denmark.     

An Abrupt Change in Tropospheric Temperature Gradient and Moisture Transport Over East Asia in the Late 1990s

ABSTRACT

Previous studies have shown that the recent summer climate (precipitation in particular) over East Asia is varying significantly. Here we extend the study to April, May, and June (AMJ) or the seasonal transition period associated with the onset of the summer monsoon. It is found that the average 1000–400?hPa AMJ tropospheric temperature (TT) experienced a sudden change at the end of the twentieth century. The change has a dipolar modal structure, with one pole over countries in Central Asia (Pakistan, Afghanistan, Uzbekistan, Kazakhstan, Kyrgyzstan, and Tajikistan.) and the other over the Tibetan Plateau. The difference in the TT between the centres of the two poles (?TT), which characterizes the zonal gradient of the TT over Asia, has seen a significant reduction since 1999. The causal relations of ?TT with the local circulation, outgoing longwave radiation (OLR), surface shortwave flux (SSWF), precipitation, etc. have been investigated using a newly developed rigorous causality analysis, which unambiguously reveals a one-way causality from ?TT to each of OLR, SSWF, and precipitation.     

Development of an airmass-based regional climate change scenario

Summary In middle latitudes, regional climates are largely determined by the frequency and character of different airmasses advected across the region. Airmass characteristics and frequencies are expected to be different in a warmer world. General circulation models are, for example, unanimous in projecting large temperature changes for high latitudes, the source region for polar airmasses. Conventional approaches to the construction of regional climate change scenarios are not able to capture such differences between airmasses. Here we present a new approach that assigns each day in the observed and model-produced records to one of three classes based on the upper-level flow, the steering current for airmasses. This approach permits an evaluation of a model's ability to reproduce the observed regional climate in terms of airmasses which is more insightful than a comparison of monthly means. The model used here, the CCM0 version of the NCAR model, was found to reproduce many of the observed December airflow features (the month chosen to demonstrate the approach) for the Lake Superior basin. The approach also permits a more insightful analysis of the projected changes under 2*CO₂ conditions. The CCM0 projects a significant warming and moistening only for the northerly airflows. The northerly flows are also projected to become more frequent. To illustrate the significance of these results, daily scenarios of climate change were constructed from these projections and used in a lake evaporation model. It is found that the changes in the northerly flows projected by this model translate into a 19% reduction in the evaporative power of the air over Lake Superior (wind speeds held at present level).With 3 Figures     

United States non-cooperation and the Paris agreement

In June 2017, the Trump administration decided to withdraw the US from the Paris Agreement, a landmark climate agreement adopted in 2015 by 195 nations. The exit of the US has not just raised concern that the US will miss its domestic emission reduction targets, but also that other parties to the Paris Agreement might backtrack on their initial pledges regarding emission reductions or financial contributions. Here we assess the magnitude of the threat that US non-cooperation poses to the Paris Agreement from an international relations perspective. We argue that US non-cooperation does not fundamentally alter US emissions, which are unlikely to rise even in the absence of new federal climate policies. Nor does it undermine nationally determined contributions under pledge and review, as the Paris Agreement has introduced a new logic of domestically driven climate policies and the cost of low-carbon technologies keeps falling. However, US non-participation in raising climate finance could raise high barriers to global climate cooperation in the future. Political strategies to mitigate these threats include direct engagement by climate leaders such as the European Union with key emerging economies, notably China and India, and domestic climate policies that furnish benefits to traditional opponents of ambitious climate policy.

Key policy insights

• US non-cooperation need not be a major threat to pledge and review under the Paris Agreement.

• US non-cooperation is a serious threat to climate finance.

• Deeper engagement with emerging economies offers new opportunities for global climate policy.

     

A conceptual framework for cross-border impacts of climate change

Climate change impacts, adaptation and vulnerability studies tend to confine their attention to impacts and responses within the same geographical region. However, this approach ignores cross-border climate change impacts that occur remotely from the location of their initial impact and that may severely disrupt societies and livelihoods. We propose a conceptual framework and accompanying nomenclature for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact propagate through a response transmission system. A key to understanding cross-border impacts and responses is a recognition of different types of climate triggers, categories of cross-border impacts, the scales and dynamics of impact transmission, the targets and dynamics of responses and the socio-economic and environmental context that also encompasses factors and processes unrelated to climate change. These insights can then provide a basis for identifying relevant causal relationships. We apply the framework to the floods that affected industrial production in Thailand in 2011, and to projected Arctic sea ice decline, and demonstrate that the framework can usefully capture the complex system dynamics of cross-border climate impacts. It also provides a useful mechanism to identify and understand adaptation strategies and their potential consequences in the wider context of resilience planning. The cross-border dimensions of climate impacts could become increasingly important as climate changes intensify. We conclude that our framework will allow for these to be properly accounted for, help to identify new areas of empirical and model-based research and thereby support climate risk management.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

The socialization potential of the CDM in EU–Vietnam climate relations

This article argues that the material incentives associated with climate policies such as the Clean Development Mechanism (CDM) may contribute to the socialization of emerging economies such as Vietnam in economic-oriented climate change norms. In current academic research, the CDM has both been extolled as a cost-effective and vilified as an environmentally inadequate instrument. Few studies so far, however, have looked into the CDM's potential contribution to socialization-related phenomena such as raising climate change awareness. This article aims to fill that gap by studying the CDM in EU–Vietnam relations in four periods, namely initiation (2001–2007), improvement (2008–2010), consolidation (2010–2012), and potential habit formation (2012 and beyond), with both the EU and Vietnam being important players in the market for CDM credits (Certified Emission Reductions or CERs). We argue that there is at least a strong potential for habit formation resulting from the CDM's material incentives, and that the underlying causal mechanism involves the emergence and activities of norm entrepreneurs and habit formation through a process of legal institutionalization.

Policy relevance

Normative transformation or change is increasingly attracting the attention of both climate policy makers and scholars alike, certainly in view of the failures of ‘standard’ economic or technological solutions to tackle climate change. There is a need, however, to apply insights from social theory to specific policies and cases. The policy relevance of this article lies here: does the CDM (a specific policy) affect climate concerns (norms) in Vietnam (a specific case)? And, if so, to what extent and why? Based on previous research regarding the Chinese case, it is expected that the CDM's material incentives result in a mild effect in Vietnam, probably less pronounced than in China in view of the latter's relative level of economic development, and the strength of its political and legal-institutional system and (human) capacity to develop CDM projects. This article's research findings point out that whether and how ‘deep’ these new shared ideas will succeed in becoming standards of appropriate behaviour in Vietnam might to some extent depend on whether the international community is able to offer a material incentive structure that fosters such a normative transformation.     

Multivariate and multiscale dependence in the global climate system revealed through complex networks

A systematic characterization of multivariate dependence at multiple spatio-temporal scales is critical to understanding climate system dynamics and improving predictive ability from models and data. However, dependence structures in climate are complex due to nonlinear dynamical generating processes, long-range spatial and long-memory temporal relationships, as well as low-frequency variability. Here we utilize complex networks to explore dependence in climate data. Specifically, networks constructed from reanalysis-based atmospheric variables over oceans and partitioned with community detection methods demonstrate the potential to capture regional and global dependence structures within and among climate variables. Proximity-based dependence as well as long-range spatial relationships are examined along with their evolution over time, yielding new insights on ocean meteorology. The tools are implicitly validated by confirming conceptual understanding about aggregate correlations and teleconnections. Our results also suggest a close similarity of observed dependence patterns in relative humidity and horizontal wind speed over oceans. In addition, updraft velocity, which relates to convective activity over the oceans, exhibits short spatiotemporal decorrelation scales but long-range dependence over time. The multivariate and multi-scale dependence patterns broadly persist over multiple time windows. Our findings motivate further investigations of dependence structures among observations, reanalysis and model-simulated data to enhance process understanding, assess model reliability and improve regional climate predictions.