Rainfall in Queensland: Part 4: the ability of HiGEM to simulate Queensland's rainfall variability and its drivers

Stakeholders and policymakers are seeking detailed information on the impacts of climate change and variability, especially on rainfall, at the local and regional levels. This information can be delivered by only those global climate models that represent the atmosphere and ocean at fine resolution, to robustly simulate the weather systems that produce rainfall. These models provide us with a better understanding of the key meteorological phenomena that affect Queensland. That knowledge can then be used to improve the simulation of these phenomena in lowerresolution climate models. Implementing these improvements will provide more accurate predictions on weekly to seasonal and decadal timescales, as well as more robust predictions of the impacts of climate change on these phenomena. High-resolution Global Environment Model, version 1.1 (HiGEM) is a global, coupled climate model that was developed by the U.K. academic community. It is based on the U.K. Hadley Centre's HadGEM1 model, but HiGEM has considerably higher resolution: 90 km in the atmosphere and 30 km in the ocean. HiGEM has been used in this research as its increased resolution may allow the model to better represent regional climate variability and change in Queensland. In this research, a 150 year control simulation of HiGEM was assessed to evalute the ability of the model to simulate Queensland's rainfall and its inter-annual and decadal variability. HiGEM was also assessed for its ability to produce the observed Empirical Orthogonal Teleconnection (EOT) patterns of rainfall avariability obtained from the SILO gridded rainfall dataset. In the mean, HiGEM produces less rainfall over Queensland than observed, particularly in the north of the state. Most of this dry bias occurs because the model simulates a weaker Australian summer monsoon than is observed. However, HiGEM represents well the relationship between the El Niño Southern Oscillation (ENSO) and Queensland rainfall, on annual and seasonal timescales. The model even captured the observed asymmetric correlation between the ENSO and Queensland rainfall: stronger La Niña events cause stronger flood years in Queensland, but stronger El Niño events do not cause stronger droughts. The research found that HiGEM lacks the ability to model decadal variations in Queensland rainfall and in the teleconnection between the ENSO and rainfall. This is likely due to the model's inability to simulate the Interdecadal Pacific Oscillation (IPO), which has been identified as the key driver of these variations. In relation to the generation of tropical cyclones, HiGEM captures the observed regions of tropical-cyclone formation and the correct distributions of tropical cyclone tracks, but simulates too many tropical cyclones in the Southwest Pacific. When EOT analysis is applied to HiGEM and the results are compared with the EOT patterns computed using observed rainfall, HiGEM performs well for those EOTs related to the ENSO in summer, winter and spring. HiGEM also represents the relationship between Southeast Queensland rainfall and onshore easterly winds, including the decadal variations in the winds' strength and moisture content. Futher, HiGEM correctly simulates the observed association between the frequency of tropical cyclones and summer rainfall in Cape York. The success of HiGEM at reproducing many of the observed EOTs, particularly in summer, increases our confidence in the model's ability to predict the impact of climate change on Queensland's rainfall and its drivers

A comparison of three canopy interception models for a leafless mixed deciduous forest stand in the eastern United States

Canopy interception of incident precipitation is a critical component of the forest water balance during each of the four seasons. Models have been developed to predict precipitation interception from standard meteorological variables because of acknowledged difficulty in extrapolating direct measurements of interception loss from forest to forest. No known study has compared and validated canopy interception models for a leafless deciduous forest stand in the eastern United States. Interception measurements from an experimental plot in a leafless deciduous forest in northeastern Maryland (39°42'N, 75°5'W) for 11 rainstorms in winter and early spring 2004/05 were compared to predictions from three models. The Mulder model maintains a moist canopy between storms. The Gash model requires few input variables and is formulated for a sparse canopy. The WiMo model optimizes the canopy storage capacity for the maximum wind speed during each storm. All models showed marked underestimates and overestimates for individual storms when the measured ratio of interception to gross precipitation was far more or less, respectively, than the specified fraction of canopy cover. The models predicted the percentage of total gross precipitation (PG) intercepted to within the probable standard error (8.1%) of the measured value: the Mulder model overestimated the measured value by 0.1% of PG; the WiMo model underestimated by 0.6% of PG; and the Gash model underestimated by 1.1% of PG. The WiMo model’s advantage over the Gash model indicates that the canopy storage capacity increases logarithmically with the maximum wind speed. This study has demonstrated that dormant-season precipitation interception in a leafless deciduous forest may be satisfactorily predicted by existing canopy interception models

Rainfall in Queensland: Part 5: projected changes in Queensland rainfall under double-CO2 conditions in the HiGEM model

This report analyses projected changes in Queensland rainfall from the HiGEM global climate model under atmospheric carbon dioxide (CO2) concentrations of approximately 690 parts per million (ppm), equivalent to the CO2 concentration in the late 21st century under a moderate (IPCC SRES (2012) A1B) emissions scenario. HiGEM is a high-resolution version of the U.K. Met Office model (HadGEM1). Previously reported research found that with present-day CO2, HiGEM accurately simulated many observed climate drivers of Queensland rainfall, including the El Niño Southern Oscillation; this increases our confidence in the model’s projections of rainfall change. The HiGEM model projects that average surface temperatures in Queensland will warm by approximately 2°C under doubled CO2, with the strongest warming in autumn and winter. Consistent with many other studies, the land warms by more than the ocean, leading to greater warming inland in Queensland and less along the coast. While HiGEM projects small changes to annual-total rainfall, the November–April wet season becomes compressed: 10–20 per cent more rain falls during January and February, with 10–40 per cent less in November, March and April. The Queensland wet season begins up to 10 days later—particularly along the coast—and ends up to 20 days earlier—particularly in the southwest. Precipitation falls in fewer but much more intense events. The HiGEM model projects that Queensland will rely more strongly upon heavy mid-summer rains for its annual precipitation. This has important consequences for agriculture and for water storage. The frequency of extreme rain days (greater than 100 millimetre accumulation) in HiGEM increases by up to 40 per cent, particularly in summer during the intensified monsoon. HiGEM also projects that the average duration of extreme rainfall will rise (by 20 per cent) as will the area covered by each event (15 per cent). The number of light rain days (1–5 millimetre) is projected to decrease by 5–10 per cent. Tropical cyclones become slightly less frequent near Queensland. The HiGEM model projects that many climate drivers of rainfall will remain robust in a warmer world. The correlation with ENSO declines, but there are an inadequate number of ENSO events in the future-climate simulation on which to base firm conclusions. Rainfall variations in southwest and southeast Queensland become less connected to those in the rest of the state, mostly due to an earlier end of the wet season in those regions

Diagnosing ocean feedbacks to the BSISO: SST-modulated surface fluxes and the moist static energy budget

The oceanic feedback to the atmospheric boreal summer intraseasonal oscillation (BSISO) is examined by diagnosing the sea surface temperature (SST) modification of surface fluxes and the moist static energy (MSE) on intraseasonal scales. SST variability affects intraseasonal surface latent heat (LH) and sensible heat (SH) fluxes, through its influence on air-sea moisture and temperature gradients (delta-q and delta-T). According to bulk formula decomposition, LH is mainly determined by wind-driven flux perturbations, while SH is more sensitive to thermodynamic flux perturbations. SST fluctuations tend to increase the thermodynamic flux perturbations over active BSISO regions, but this is largely offset by the wind-driven flux perturbations. Enhanced surface fluxes induced by intraseasonal SST anomalies are located ahead (north) of the convective center over both the Indian Ocean and western Pacific, favoring BSISO northward propagation. Analysis of budgets of column-integrated MSE () and its time rate of change (d/dt) show that SST-modulated surface fluxes can influence the development and propagation of the BSISO, respectively. LH and SH variability induced by intraseasonal SSTs maintain 1-2% of /day over the equatorial western Indian Ocean, Arabian Sea and Bay of Bengal, but damp about 1% of /day over the western North Pacific. The contribution of intraseasonal SST variability to d/dt can reach 12-20% over active BSISO regions. These results suggest that SST variability is conducive, but perhaps not essential, for the propagation of convection during the BSISO life cycle

The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

The fidelity of the simulated Indian summer monsoon is analysed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML2.0) in terms of its boreal summer mean state and propagation of the boreal summer intraseasonal oscillation (BSISO). The model produces substantial biases in mean June–September precipitation, especially over India, in common with other MetUM configurations. Using a correction technique to constrain the mean seasonal cycle of ocean temperature and salinity, the effects of regional air–sea coupling and atmospheric horizontal resolution are investigated. Introducing coupling in the Indian Ocean degrades the atmospheric basic state compared with prescribing the observed seasonal cycle of sea surface temperature (SST). This degradation of the mean state is attributable to small errors (±0.5°C) in mean SST. Coupling slightly improves some aspects of the simulation of northward BSISO propagation over the Indian Ocean, Bay of Bengal, and India, but degrades others. Increasing resolution from 200 to 90km grid spacing (approximate value at the Equator) improves the atmospheric mean state, but increasing resolution again to 40km offers no substantial improvement. The improvement to intraseasonal propagation at finer resolution is similar to that due to coupling

Atmospheric circulation patterns associated with extreme cold winters in the UK

This study examines the atmospheric circulation patterns and surface features associated with the seven coldest winters in the U.K. since 1870, using the 20th Century Reanalysis. Six of these winters are outside the scope of previous reanalysis datasets; we examine them here for the first time. All winters show a marked lack of the climatological southwesterly flow over the UK, displaying easterly and northeasterly anomalies. Six of the seven winters (all except 1890) were associated with a negative phase of the North Atlantic Oscillation; 1890 was characterised by a blocking anticyclone over and northeast of the UK

ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models

General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent, and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and timestep. We introduce a set of diagnostics (ASoP1) to compare the spatial and temporal scales of precipitation across GCMs and observations, which can be applied to data ranging from the gridscale and timestep to regional and sub-monthly averages. ASoP1 measures the spectrum of precipitation intensity, temporal variability as a function of intensity, and spatial and temporal coherence. When applied to timestep, gridscale tropical precipitation from ten GCMs, the diagnostics reveal that far from the "dreary" persistent light rainfall implied by daily mean data, most models produce a broad range of timestep intensities that span 1-100 mm/day. Models show widely varying spatial and temporal scales of timestep precipitation. Several GCMs show concerning quasi-random behavior that may influence alter the spectrum of atmospheric waves. Averaging precipitation to a common spatial (~600 km) or temporal (3-hr) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behavior. When compared against satellite-derived analyses at these scales, all models produce features that are too large and too persistent

The effect of horizontal resolution on the representation of the global monsoon annual cycle in Atmospheric General Circulation Models

The sensitivity of the representation of the global monsoon annual cycle to horizontal resolution is compared in three Atmospheric General Circulation Models (AGCMs): the Met Office Unified Model-Global Atmosphere 3.0 (MetUM-GA3), the Meteorological Research Institute AGCM3 (MRI-AGCM3) and Global High Resolution AGCM from the Geophysical Fluid Dynamics Laboratory (GFDL-HiRAM). For each model, we use two horizontal resolution configurations for the period 1998–2008. Increasing resolution consistently improves simulated precipitation and low-level circulation of the annual mean and the first two annual cycle modes, as measured by pattern correlation coefficient and Equitable Threat Score. Improvements in simulating the summer monsoon onset and withdrawal are region-dependent. No consistent response to resolution is found in simulating summer monsoon retreat. Regionally, increased resolution reduces the positive bias in simulated annual mean precipitation, the two annual-cycle modes over the West African monsoon and Northwestern Pacific monsoon. An overestimation of the solstitial mode and an underestimation of the equinoctial asymmetric mode of the East Asian monsoon are reduced in all high-resolution configurations. Systematic errors exist in lower-resolution models for simulating the onset and withdrawal of the summer monsoon. Higher resolution models consistently improve the early summer monsoon onset over East Asia and West Africa, but substantial differences exist in the responses over Indian monsoon region, where biases differ across the three low-resolution AGCMs. This study demonstrates the importance of a multi-model comparison when examining the added value of resolution and the importance of model physical parameterizations for the Indian monsoon simulation

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

This study analyses tropical rainfall variability, on a range of temporal and spatial scales, in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, compared with two satellite-derived rainfall datasets. We focus on the shorter scales i.e. from the native grid and time-step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time-step is largely independent of the grid-box size and time-step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time-step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid-boxes/time-steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ~100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ~10 day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling the small/short scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of the small/short scale processes

Quasi-stationary waves and their impact on European weather and extreme events

Large-scale, quasi-stationary atmospheric waves (QSWs) have long been known to be associated with weather extremes such as the European heatwave in 2003. There is much debate in the scientific literature as to whether QSW activity may increase under a changing climate, providing a strong motivation for developing a better understanding of the behaviour and drivers of QSWs. This paper presents the first steps in this regard: the development of a robust objective method for a simple identification and characterisation of these waves. A clear connection between QSWs and European weather and extreme events is confirmed for all seasons, indicating that blocking anticyclones are often part of a broader scale wave pattern. Investigation of the QSW climatology in the Northern Hemisphere reveals that wave activity is typically strongest in midlatitudes, particularly at the exit of the Atlantic and Pacific storm track with weaker intensities in summer. In general, the structure of individual QSW events tends to follow the climatological pattern, except in winter where the strongest and most persistent QSWs are typically shifted polewards, indicating a distinct evolution of the ’strongest’ QSW events. Modes of inter-annual variability are calculated to better understand their importance and connection to European temperatures and to identify relevant QSW patterns. This analysis highlights that European winter temperatures are strongly associated with the meridional location of QSW activity whereas warm European summer temperatures are associated with increases in the overall intensity of midlatitude QSW activity. QSWs are shown to be strongly connected to commonly used indices to describe the large scale atmospheric circulation (NAO, AO, Ni˜no 3.4, PNA) but offer a more direct link to understanding their impact on regional weather events. It is therefore hoped that objective identification of QSWs will provide a useful new viewpoint for interpreting large-scale weather alongside more traditional measures and metrics