Climatological Variance Research Articles

Agronomic analysis of WaPOR applications: Confirming conservative biomass water productivity in inherent and climatological variance of WaPOR data outputs

CONTEXTImprovements in agricultural water productivity with constrained water resources are often regarded a prerequisite to meet food demands of a growing world population. The WaPOR data portal was launched to monitor biomass, evapotranspiration and biomass water productivity in Africa and the Near East using remote sensing technologies. The WaPOR database shows spatial pixel variation in biomass, suggesting scope to improve water productivity at field level. OBJECTIVEThe aim of this paper is to assess with regression analyses for different spatial and temporal scales whether spatial variability in biomass and evapotranspiration as revealed by WaPOR can be attributed to human influenceable factors, variations in local climate, or methodologically inherent inacuracies of the WaPOR data. METHODSVariation in biomass and evapotranspiration data was assessed through agronomic linear regression analyses, for two large-scale irrigated sugarcane estates in Ethiopia (Wonji) and Mozambique (Xinavane). RESULTS AND CONCLUSIONSIn these cases 82–94% of the variation in biomass and evapotranspiration is attributed to crop photosynthetic efficiency (very large influence), local climate (large influence) and irrigation technology (small influence). The remaining unexplained spatial variability is small (6–18%) and falls within an error range of +/− 9%. In conclusion, WaPOR performed very well by neatly reproducing the conservative relationship between biomass and evapotranspiration, which also means there is very limited scope to improve biomass water productivity through WaPOR monitoring. Further research is recommended on the magnitude of WaPOR accuracy and other sources that explain variations in biomass and evapotranspiration. SIGNIFCANCEApplicability of the WaPOR database to monitor biomass water productivity was assessed. Spatial variability in biomass and evapotranspiration data largely stemmed from photosynthesis and local climate, factors farmers and water managers can hardly influence.

Machine Learning–Based Hurricane Wind Reconstruction

Abstract Here we present a machine learning–based wind reconstruction model. The model reconstructs hurricane surface winds with XGBoost, which is a decision-tree-based ensemble predictive algorithm. The model treats the symmetric and asymmetric wind fields separately. The symmetric wind field is approximated by a parametric wind profile model and two Bessel function series. The asymmetric field, accounting for asymmetries induced by the storm and its ambient environment, is represented using a small number of Laplacian eigenfunctions. The coefficients associated with Bessel functions and eigenfunctions are predicted by XGBoost based on storm and environmental features taken from NHC best-track and ERA-Interim data, respectively. We use HWIND for the observed wind fields. Three parametric wind profile models are tested in the symmetric wind model. The wind reconstruction model’s performance is insensitive to the choice of the profile model because the Bessel function series correct biases of the parametric profiles. The mean square error of the reconstructed surface winds is smaller than the climatological variance, indicating skillful reconstruction. Storm center location, eyewall size, and translation speed play important roles in controlling the magnitude of the leading asymmetries, while the phase of the asymmetries is mainly affected by storm translation direction. Vertical wind shear impacts the asymmetry phase to a lesser degree. Intended applications of this model include assessing hurricane risk using synthetic storm event sets generated by statistical–dynamical downscaling hurricane models.

Application of a combined standard deviation and mean based approach to MOPITT CO column data, and resulting improved representation of biomass burning and urban air pollution sources

This work presents a new methodology to simultaneously account for both the mean and variance of 17 years of Carbon Monoxide [CO] measurements from the MOPITT satellite (from 2000 to 2016) over Southeast and East Asia. We demonstrate that the new technique is stable and produces a set of results which are both consistent with the understood geographical and temporal distribution of CO sources, as well as those of some other co-emitted species. These regions were chosen because they have high levels of CO loadings and complex factors driving their underlying emissions profiles. We first successfully categorize the region, based on the total CO column measurements, into those locations impacted by intense urbanization (high climatological mean, low climatological variance, high loading throughout most of the year, and variation mostly due to global-scale chemistry and climatology), large-scale biomass burning (low climatological mean, high climatological variance, mostly clean but with short and intense peak events occurring around a similar time year-to-year), those regions undergoing a significant change from one type to another, and those regions which are clean. We further reproduce the temporal and spatial distributions of other co-emitted species measured by other measurement platforms, including aerosols (AOD) and gasses (NO2), and demonstrate consistency across all three platforms. Third of all, we produce an important scientific finding using these new results, in terms of a significant geographic expansion of the known biomass burning regions from Myanmar through Northern Vietnam, as compared with previous research. Fourth, we also find that urbanization dominates emissions of CO over both known urban agglomerations in East and Southeast Asia, including around Beijing, Shanghai, the Pearl River Delta, Seoul, Shandong, Chengdu, Chongqing, Hanoi and Bangkok, but also extend into otherwise unclassified or recently emerging mega-cities including Shanxi Province, Henan Province, and Liaoning Province. Additionally, we have found that there is a 37% overlap with emissions from FINN, providing both validation of the technique over known biomass burning regions, and an ability to quantify regions that are burning but not presently identified. Finally, we have a consistent and value-adding comparison and contrast with an EOF/PCA analysis in terms of both our regions classified as biomass burning and mixed/urban, with each approach offering its unique advantages and drawbacks. Although our approach has made some very simple assumptions in terms of cutoff that can and should be improved by the community, its robust results provide new insights into the rapid changes impacting CO throughout Asia, and will allow models to have an improved chance at representing peak events or areas undergoing rapid change.

Interannual Variability of the Australian Summer Monsoon System Internally Sustained Through Wind‐Evaporation Feedback

AbstractInterannual variability of Australian summer monsoon (AUSM) activity is hardly forced locally or remotely by tropical sea surface temperature (SST) variability. Despite this lack of SST forcing, convection variability in northern Australia is so strong that it emerges as a distinct peak in climatological variance of convection in austral summer. The present study shows that an internal variability unforced by tropical SST anomaly is dominant in seasonal mean strength of the AUSM system. A mechanism that sustains convection anomaly without SST forcing is also examined. Analysis of latent heat flux reveals that under the climatological monsoon westerlies, the wind‐evaporation feedback in the tropical southeastern Indian Ocean sustains anomalous convection despite a counteracting effect of SST anomalies. The wind anomalies induced by the anomalous AUSM change the subsurface southeastern Indian Ocean, which can contribute to the maintenance of the anomalous convection through weakening the damping effect by SST anomalies.

Snow Avalanche Susceptibility Based Assessment of Release Zones over Complex Terrain of Siachen Glacier Applying Ramms and Dsr as Active Macroclimatic Factor

Glaciers are a critical component of the earth system and environment, both in Polar regions and high altitude terrains. At glaciated terrain, local physiographical and climatological variance causes natural hazard which can lead to local and regional consequences. However, focusing the local complex terrain, the snow avalanches are not easily predictable. RAMMS (Rapid mass movement) application has applied successfully to determine the release areas for snow avalanche dimensions, yet the local microclimatic parameters have not been considered. To assess and identify the susceptible zones on basis of snow avalanche susceptibility, we applied RAMMS-avalanche simulation for avalanche dimension and MTLCIM-XL (Mountain Microclimate) simulator to calculate downward shortwave radiation (DSR) integration as the active microclimatic factor, at target release areas over the Siachen glacier. We obtain high dimension values (above assume a maximum value of pressure 50-80kPa) for the selected release areas from the resultant simulation of snow avalanche maximum dimension. Additional integrative approach base assessment of DSR calculation revealed high values (threshold of daily mean ≥ 350-400W/m2) specifically over three release zones for critical month of April (daily mean based). Prospectively, the conclusive susceptibility analysis depicts the target release areas as potential zones for snow avalanche. In conclusion, the combined approach of RAMMS avalanche simulation with the consideration of local microclimatic parameters show better results; and will need to be considered in future studies of snow avalanche susceptibility and preparedness.

Optimisation of an idealised primitive equation ocean model using stochastic parameterization

Using a simple parameterization, an idealised low resolution (biharmonic viscosity coefficient of 5 × 1012 m4s−1, 128 × 128 grid) primitive equation baroclinic ocean gyre model is optimised to have a much more accurate climatological mean, variance and response to forcing, in all model variables, with respect to a high resolution (biharmonic viscosity coefficient of 8 × 1010 m4s−1, 512 × 512 grid) equivalent. For example, the change in the climatological mean due to a small change in the boundary conditions is more accurate in the model with parameterization. Both the low resolution and high resolution models are strongly chaotic.We also find that long timescales in the model temperature auto-correlation at depth are controlled by the vertical temperature diffusion parameter and time mean vertical advection and are caused by short timescale random forcing near the surface.This paper extends earlier work that considered a shallow water barotropic gyre. Here the analysis is extended to a more turbulent multi-layer primitive equation model that includes temperature as a prognostic variable. The parameterization consists of a constant forcing, applied to the velocity and temperature equations at each grid point, which is optimised to obtain a model with an accurate climatological mean, and a linear stochastic forcing, that is optimised to also obtain an accurate climatological variance and 5 day lag auto-covariance. A linear relaxation (nudging) is not used. Conservation of energy and momentum is discussed in an appendix.

Ensemble Forecasts: Probabilistic Seasonal Forecasts Based on a Model Ensemble

Ensembles of general circulation model (GCM) integrations yield predictions for meteorological conditions in future months. Such predictions have implicit uncertainty resulting from model structure, parameter uncertainty, and fundamental randomness in the physical system. In this work, we build probabilistic models for long-term forecasts that include the GCM ensemble values as inputs but incorporate statistical correction of GCM biases and different treatments of uncertainty. Specifically, we present, and evaluate against observations, several versions of a probabilistic forecast for gridded air temperature 1 month ahead based on ensemble members of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2). We compare the forecast performance against a baseline climatology based probabilistic forecast, using average information gain as a skill metric. We find that the error in the CFSv2 output is better represented by the climatological variance than by the distribution of ensemble members because the GCM ensemble sometimes suffers from unrealistically little dispersion. Lack of ensemble spread leads a probabilistic forecast whose variance is based on the ensemble dispersion alone to underperform relative to a baseline probabilistic forecast based only on climatology, even when the ensemble mean is corrected for bias. We also show that a combined regression based model that includes climatology, temperature from recent months, trend, and the GCM ensemble mean yields a probabilistic forecast that outperforms approaches using only past observations or GCM outputs. Improvements in predictive skill from the combined probabilistic forecast vary spatially, with larger gains seen in traditionally hard to predict regions such as the Arctic.

The MJO skeleton model with observation‐based background state and forcing

The Madden–Julian Oscillation (MJO) skeleton model, a nonlinear oscillator model, has previously been shown to have solutions which exhibit several features in common with observed MJOs. In prior studies, model solutions have been found using mathematically simple (constant or sinusoidal) identical moistening and radiative forcing functions. Here we investigate whether this model can also produce realistic regional variability. To do this, observation‐based forcing functions are prescribed for latent heat flux and radiative cooling, and model solutions –both linear and nonlinear –are studied. In the stochastic nonlinear model, solutions reproduce the climatological mean convective activity well and the climatological variance reasonably well for such a simple model. In the linearized model, the solutions are found to contain additional structure including a realistic wave envelope of convective activity centred over the warm pool. These linear solutions are then used to identify and compare significant MJO activity in both reanalysis data and stochastic model solutions. Additional potential uses for these analytical and numerical solutions are discussed.

On the impact of re‐centring initial conditions for ensemble forecasts

Many weather forecasting centres generate the initial conditions for their ensemble prediction system by adding a set of perturbations to a single analysis. At the European Centre for Medium‐Range Weather Forecasts (ECMWF), initial conditions from an ensemble of data assimilations (EDA) are re‐centred on the deterministic high‐resolution analysis. This study assesses how the re‐centring step affects the ensemble forecasts and what benefits starting directly from the perturbed EDA members may bring. Numerical experiments are based on suitably modified configurations of the ECMWF medium‐range ensemble.Re‐centring leads to an artificial increase of the climatological variance of the initial conditions for variables and spatial scales that are not well constrained in the analysis. For those variables and spatial scales, the EDA variance can reach a similar magnitude as the climatological variance. Re‐centring on the unperturbed member of the EDA leads to a degradation of the ensemble skill in comparison to starting directly from the perturbed EDA members. Depending on the amplitude of the EDA spread and which scales are less constrained, different lead times are affected. Furthermore, the re‐centring step increases the jumpiness of the ensemble forecasts in general and alters the precipitation frequency during the first 12 h of the forecasts.It also becomes apparent from our results that the benefit for the ECMWF ensemble of starting directly from EDA initial conditions is sensitive to several factors: the difference in analysis accuracy between the unperturbed EDA control and the high‐resolution analysis, the number of EDA members, and the EDA spread. At present, the forecast skill still benefits from centring the ensemble initial conditions on the high‐resolution analysis, but future changes to the EDA may change the balance.

Estimating the temporal domain when the discount of the net evaporation term affects the resulting net precipitation pattern in the moisture budget using a 3-D Lagrangian approach.

The Lagrangian FLEXPART model has been used during the last decade to detect moisture sources that affect the climate in different regions of the world. While most of these studies provided a climatological perspective on the atmospheric branch of the hydrological cycle in terms of precipitation, none assessed the minimum temporal domain for which the climatological approach is valid. The methodology identifies the contribution of humidity to the moisture budget in a region by computing the changes in specific humidity along backward (or forward) trajectories of air masses over a period of ten days beforehand (afterwards), thereby allowing the calculation of monthly, seasonal and annual averages. The current study calculates as an example the climatological seasonal mean and variance of the net precipitation for regions in which precipitation exceeds evaporation (E-P<0) for the North Atlantic moisture source region using different time periods, for winter and summer from 1980 to 2000. The results show that net evaporation (E-P>0) can be discounted after when the integration of E-P is done without affecting the general net precipitation patterns when it is discounted in a monthly or longer time scale.

Ensemble Kalman filters for dynamical systems with unresolved turbulence

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (a multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution.The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k−5/6 (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.

Coupled Air–Mixed Layer Temperature Predictability for Climate Reconstruction

Abstract A central issue for understanding past climates involves the use of sparse time-integrated data to recover the physical properties of the coupled climate system. This issue is explored in a simple model of the midlatitude climate system that has attributes consistent with the observed climate. A quasigeostrophic (QG) model thermally coupled to a slab ocean is used to approximate midlatitude coupled variability, and a variant of the ensemble Kalman filter is used to assimilate time-averaged observations. The dependence of reconstruction skill on coupling and thermal inertia is explored. Results from this model are compared with those for an even simpler two-variable linear stochastic model of midlatitude air–sea interaction, for which the assimilation problem can be solved semianalytically. Results for the QG model show that skill decreases as the length of time over which observations are averaged increases in both the atmosphere and ocean when normalized against the time-averaged climatological variance. Skill in the ocean increases with slab depth, as expected from thermal inertia arguments, but skill in the atmosphere decreases. An explanation of this counterintuitive result derives from an analytical expression for the forecast error covariance in the two-variable stochastic model, which shows that the ratio of noise to total error increases with slab ocean depth. Essentially, noise becomes trapped in the atmosphere by a thermally stiffer ocean, which dominates the decrease in initial condition error owing to improved skill in the ocean. Increasing coupling strength in the QG model yields higher skill in the atmosphere and lower skill in the ocean, as the atmosphere accesses the longer ocean memory and the ocean accesses more atmospheric high-frequency “noise.” The two-variable stochastic model fails to capture this effect, showing decreasing skill in both the atmosphere and ocean for increased coupling strength, due to an increase in the ratio of noise to the forecast error variance. Implications for the potential for data assimilation to improve climate reconstructions are discussed.

A minimal model of the Atlantic Multidecadal Variability: its genesis and predictability

Through a box model of the subpolar North Atlantic, we examine the genesis and predictability of the Atlantic Multidecadal Variability (AMV), posited as a linear perturbation sustained by the stochastic atmosphere. Postulating a density-dependent thermohaline circulation (THC), the latter would strongly differentiate the thermal and saline damping, and facilitate a negative feedback between the two fields. This negative feedback preferentially suppresses the low-frequency thermal variance to render a broad multidecadal peak bounded by the thermal and saline damping time. We offer this “differential variance suppression” as an alternative paradigm of the AMV in place of the “damped oscillation”—the latter generally not allowed by the deterministic dynamics and in any event bears no relation to the thermal peak. With the validated dynamics, we then assess the AMV predictability based on the relative entropy—a difference of the forecast and climatological probability distributions, which decays through both error growth and dynamical damping. Since the stochastic forcing is mainly in the surface heat flux, the thermal noise grows rapidly and together with its climatological variance limited by the THC-aided thermal damping, they strongly curtail the thermal predictability. The latter may be prolonged if the initial thermal and saline anomalies are of the same sign, but even rare events of less than 1% chance of occurrence yield a predictable time that is well short of a decade; we contend therefore that the AMV is in effect unpredictable.

On the Effect of Correlations on Rank Histograms: Reliability of Temperature and Wind Speed Forecasts from Finescale Ensemble Reforecasts

Abstract The rank histogram (RH) is a visual tool for assessing the reliability of ensemble forecasts (i.e., the degree to which the forecasts and the observations have the same distribution). But it is already known that in certain situations it conveys misleading information. Here, it is shown that a temporal correlation can lead to a misleading RH, but such a correlation contributes only to the sampling variability of the RH, and so it is accounted for by producing a RH that explicitly displays sampling variability. A simulation is employed to show that the variance within each ensemble member (i.e., climatological variance), the correlation between ensemble members, and the correlation between the observations and the forecasts, all have a confounding effect on the RH, making it difficult to use the RH for assessing the climatological component of forecast reliability. It is proposed that a “residual” quantile–quantile plot (denoted R-Q-Q plot) is better suited than the RH for assessing the climatological component of forecast reliability. Then, the RH and R-Q-Q plots for temperature and wind speed forecasts at 90 stations across the continental United States are computed. A wide range of forecast reliability is noted. For some stations, the nonreliability of the forecasts can be attributed to bias and/or under-or overclimatological dispersion. For others, the difference between the distributions can be traced to lighter or heavier tails in the distributions, while for other stations the distributions of the forecasts and the observations appear to be completely different. A spatial signature is also noted and discussed briefly.

Model Fidelity versus Skill in Seasonal Forecasting

Abstract The relation between skill and fidelity of seasonal mean hindcasts of surface temperature by seven coupled atmosphere–ocean models is investigated. By definition, fidelity measures the agreement between model and observational climatological distributions, and skill measures the agreement between hindcasts and their corresponding verifications. While a relation between skill and fidelity seems intuitively plausible, it has not been checked systematically, nor is it mandated mathematically. New measures of skill and fidelity based on information theory are proposed. Specifically, fidelity is measured by the area average relative entropy between the climatological distributions of the model and observations, and skill is measured by the area averaged mutual information between forecast and verification. The fidelity measure is found to be dominated by the term measuring mean bias; that is, the discrepancy in climatological means is much larger than the discrepancy in climatological variances. Moreover, the mean bias is negatively correlated with skill at most initial months, lead times, and regions examined. Thus, models that more closely replicate the observed climatological mean tend to have better skill.

Comments on “Finite Samples and Uncertainty Estimates for Skill Measures for Seasonal Prediction”

Different skill measures present differing dependence on the intrinsic predictability level as well as differing dependence on sample size. Kumar (2009) computed the expected skill of idealized forecasts with varying levels of predictability using the anomaly correlation (AC), the Heidke skill score (HSS), and the ranked probability skill score (RPSS) as skill measures. An important consideration for seasonal climate forecasts, as demonstrated by Kumar (2009), is that these skill measures may vary significantly from their expected values when they are computed from relatively short forecast histories. In carrying out his skill measure characterizations, Kumar (2009) assumed that the forecast variance was equal to the climatological variance. Additionally, when computing average skill scores, forecast distributions were assumed to have identical means (signals) for a given predictability level. Here we examine some implications of those assumptions. This comment is organized as follows. In section 2 we present a perfect model framework for examining predictability. A natural requirement for predictability is that the forecast and climatological distributions be different. In the case of joint normal distributions, the existence of predictability (positive signal variance) implies that the forecast variance is less than the climatological variance. In this case, inflating the forecast variance to be equal to the climatological variance results in underconfident forecasts and lower probabilistic skill scores. In section 3 we compute the three skill scores for a given forecast signal (single initial condition) and for a set of forecasts with a specified signal to noise variance ratio (multiple initial conditions). The expected skill of a single forecast depends on both signal level and forecast variance. The AC depends on the ratio of squared signal to forecast variance, while the dependence of HSS and RPSS on signal level and forecast variance is more complex. A consequence of this functional dependence is that the AC of a set of forecasts is equal to that of a single forecast with the signal equal to the signal standard deviation. However, there is no similar relation for HSS and RPSS, and assuming a fixed signal as in Kumar (2009) generally overestimates HSS and always overestimates RPSS. We also present a useful approximation relating the RPSS and AC of a set of forecasts.

Assimilation of time-averaged observations in a quasi-geostrophic atmospheric jet model

The problem of reconstructing past climates from a sparse network of noisy time-averaged observations is considered with a novel ensemble Kalman filter approach. Results for a sparse network of 100 idealized observations for a quasi-geostrophic model of a jet interacting with a mountain reveal that, for a wide range of observation averaging times, analysis errors are reduced by about 50% relative to the control case without assimilation. Results are robust to changes to observational error, the number of observations, and an imperfect model. Specifically, analysis errors are reduced relative to the control case for observations having errors up to three times the climatological variance for a fixed 100-station network, and for networks consisting of ten or more stations when observational errors are fixed at one-third the climatological variance. In the limit of small numbers of observations, station location becomes critically important, motivating an optimally determined network. A network of fifteen optimally determined observations reduces analysis errors by 30% relative to the control, as compared to 50% for a randomly chosen network of 100 observations.

On the Reliability and Calibration of Ensemble Forecasts

Abstract An important aspect of ensemble forecasting is that the resulting probabilities are reliable (i.e., the forecast probabilities match the observed frequencies). In the medium-range forecasting context, the literature tends to focus on the requirement that, for a reliable ensemble, the ensemble spread should be representative of the uncertainty in the mean, whereas in the seasonal forecasting context, the literature tends to focus on the requirement that, for a reliable ensemble, the ensemble forecasts should have the same climatological variance as the truth. In this note, the authors emphasize that both of these requirements are necessary for reliability and they clarify that a popular calibration method actually achieves both of these requirements.

Predictability Aspects of Global Aqua-planet Simulations with Explicit Convection

High-resolution global simulations over zonally symmetric aqua planets are examined using Fourier analysis in the zonal direction. We highlight the tropics, where the large-scale weather consists of convectively-coupled waves so that explicit convection is an especially topical novelty.Squared differences between pairs of runs grow from initially tiny values to saturation at twice the climatological variance. For wavelengths shorter than 103 km, differences saturate within about a day. For tropical long waves, the time to saturation indicates predictability for at least 2 weeks. This time scale is similar in middle latitude flow, which interacts with tropical waves in the 3D model, but it is also similar in 2D pseudo-equatorial vertical plane simulations of pure convectively coupled gravity waves. As a result, no simple conclusions can be drawn about whether tropical predictability is limited more by tropical chaos or by tropical-extratropical interactions.Difference growth appears to fill out the saturation energy spectrum in a “vertical” (up-magnitude) rather than “horizontal” (up-scale) manner. Up-scale growth thus occurs as a continuing amplification of large scales after small scales saturate, which begs the question of what sets the shape of the saturation (climatological) power spectra. Wind spectra are nearly power-law with a logarithmic slope of about -5/3 in the free troposphere, remarkably so in the 2D runs and clearly distinct from slope -2 (a null hypothesis of spectrally white wind divergence). A common interpretation of -5/3 slope - as indicative of a cascade, a steady conservative transfer of energy from source to sink scales by interactions that are local in log-wavelength space - is hard to apply to these moist tropical waves.

Time mean and variability of the scale-decomposed atmospheric water budget in a 25-year simulation of the Canadian Regional Climate Model over North America

The scaled-decomposed atmospheric water budget over North America is investigated through the analysis of 25 years of simulation by the Canadian Regional Climate Model (CRCM) driven by the NCEP–NCAR reanalyses for the period 1975–1999. The time average and time variability of the atmospheric water budget for the winter and summer seasons are decomposed into their large-scale and small-scale components to identify the added value of the regional model. For the winter season, the intra-seasonal transient-eddy variance is the main temporal variability. The large- and small-scale terms are of the same order of magnitude, and are large over both coasts and weak over the continent. For the summer season, the time–mean atmospheric water budget is rather different to that of winter, with maximum values over the south-eastern part of the continent. The summer intra-seasonal variance is about twice stronger than in winter and also dominates the variability, but the inter-monthly variance is non-negligible and can be in part associated to North American Monsoon System. Over the continent, the intra-seasonal climatological variance is dominated by the variability of the small scales. The small scales, that is those scales that are only resolved in the regional model but not in the reanalyses, contribute to the added value in a regional climate simulation. In the winter season, the added value of the CRCM is large and dominated by oceanic forcing, while in summer, it is dominant (larger than the large scales) and controlled mainly by convective processes.