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Paul R. Field and Robert Wood
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Paul R. Field and Robert Wood

Abstract

Composite mean fields and probability distribution functions (PDFs) of rain rate, cloud type and cover, cloud-top temperature, surface wind velocity, and water vapor path (WVP) are constructed using satellite observations of midlatitude cyclones from four oceanic regions (i.e., the North Pacific, South Pacific, North Atlantic, and South Atlantic). Reanalysis surface pressure fields are used to ascertain the locations of the cyclone centers, onto which the satellite fields are interpolated to give a database of ∼1500 cyclones from a two-year period (2003–04). Cyclones are categorized by their strength, defined here using surface wind speed, and by their WVP, and it is found that these two measures can explain a considerable amount of the intercyclone variability of other key variables. Composite cyclones from each of the four ocean basins exhibit similar spatial structure for a given strength and WVP. A set of nine composites is constructed from the database using three strength and three WVP ranges and is used to demonstrate that the mean column relative humidity of these systems varies only slightly (0.58–0.62) for a doubling in WVP (or equivalently a 7-K rise in sea surface temperature) and a 50% increase in cyclone strength. However, cyclone-mean rain rate increases markedly with both cyclone strength and WVP, behavior that is explained with a simple warm conveyor belt model. Systemwide high cloud fraction (tops above 440 hPa) increases from 0.23 to 0.31 as cyclone strength increases by 50%, but does not vary systematically with WVP. It is suggested that the composite fields constitute useful diagnostics for evaluating the behavior of large-scale numerical models, and may provide insight into how precipitation and clouds in midlatitude cyclones respond under a changed climate.

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Robert Wood and Paul R. Field

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Cloud horizontal size distributions from near-global satellite data, from aircraft, and from a global high-resolution numerical weather prediction model, are presented for the scale range 0.1–8000 km and are shown to be well-represented using a single power-law relationship with an exponent of β = 1.66 ±0.04 from 0.1 to 1500 km or more. At scales longer than 1500 km, there is a statistically significant scale break with fewer very large clouds than expected from the power law. The size distribution is integrated to determine the contribution to cloud cover and visible reflectance from clouds larger than a given size. Globally, clouds with a horizontal dimension of 200 km or more constitute approximately 50% of the cloud cover and 60% of the reflectance, and this result is not sensitive to the minimum size threshold assumed in the integral assuming that the power law can be extrapolated below 100-m scale. The result is also not sensitive to whether the size distribution is determined using cloud segment length or cloud area. This emphasizes the great role played by large cloud sheets in determining the earth’s albedo. On the other hand, some 15% of global cloud cover comes from clouds smaller than 10 km, thus emphasizing the broad range of cloud sizes that contribute significantly to the earth’s radiation budget. Both of these results stem from the fact that β is slightly less than 2. The data are further divided and geographical and seasonal variations in the cloud size L 50 for which clouds larger than L 50 constitute 50% of the cloud cover are determined. The largest clouds (L 50 > 300 km) are found over the midlatitude oceans, particularly in summer, and over the tropical convective regions of the west Pacific and Indian Oceans and the monsoon-influenced landmasses. The smallest clouds (L 50 < 100 km) are found over the trade wind regions of the tropics/subtropics and over arid land areas. Small variations in the exponent β contribute significantly to the variations in L50. Finally, it is shown that a bounded cascade model can faithfully simulate the observed cloud size distributions and use this to examine the effects of limiting sensor resolution (pixel size) and domain size (number of pixels across image). Sensor resolution is not found to strongly impact the cloud size distribution provided the ratio of the domain to pixel size remains greater than ~1000. Thus, previous studies with small domain–pixel size ratios may provide biased information about the true cloud size distribution, and should be interpreted with caution.

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L. C. Jackson and R. A. Wood

Abstract

Different strategies have been proposed in previous studies for monitoring the Atlantic meridional overturning circulation (AMOC). As well as arrays to directly monitor the AMOC strength, various fingerprints have been suggested to represent an aspect of the AMOC based on properties such as temperature and density. The additional value of fingerprints potentially includes the ability to detect a change earlier than a change in the AMOC itself, the ability to extend a time series back into the past, and the ability to detect crossing a threshold. In this study we select metrics that have been proposed as fingerprints in previous studies and evaluate their ability to detect AMOC changes in a number of scenarios (internal variability, weakening from increased greenhouse gases, weakening from hosing and hysteresis) in the eddy-permitting coupled climate model HadGEM3-GC2. We find that the metrics that perform best are the temperature metrics based on large-scale differences, the large-scale meridional density gradient, and the vertical density difference in the Labrador Sea. The best metric for monitoring the AMOC depends somewhat on the processes driving the change. Hence the best strategy would be to consider multiple fingerprints to provide early detection of all likely AMOC changes.

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Yinghui Liu, Jeffrey R. Key, Steve Vavrus, and Cian Woods

Abstract

Northward fluxes of moisture and sensible heat into the Arctic affect the atmospheric stability, sea ice and snow cover, clouds, and surface energy budget. Intense moisture fluxes into the Arctic are called moisture intrusions; some can lead to basinwide increases in downward longwave radiation (DLR) at the surface, called downward infrared (IR) events. Using the ERA-Interim reanalysis from 1990 to 2016, this study investigated the time evolution of cloud amount and cloud properties and their impact on the surface radiation fluxes in response to Arctic moisture intrusions and downward IR events during winter for better understanding of the Arctic moisture intrusions. A composite analysis revealed several key features: moisture intrusions produce more clouds and higher cloud liquid and ice water content; positive cloud amount anomalies can persist for over 10 days over the Arctic Ocean during downward IR events; positive high-level and middle-level cloud anomalies are evident in the early stage, and positive low-level cloud anomalies are evident in the late stage. Greater clear-sky DLR and longwave cloud radiative forcing (CRF) over the Arctic Ocean accompany the greater all-sky DLR during the downward IR events. Greater clear-sky DLR can be attributed to higher air temperatures and higher total column water vapor, while greater longwave CRF is the result of larger cloud amount and cloud water content. Longwave CRF anomalies account for approximately 40% of the all-sky DLR anomalies.

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Axel J. Schweiger, Kevin R. Wood, and Jinlun Zhang

Abstract

PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

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Dennis P. Lettenmaier, Eric F. Wood, and James R. Wallis

Abstract

Spatial patterns in trends of four monthly variables: average temperature, precipitation, streamflow, and average of the daily temperature range were examined for the continental United States for the period 1948–88. The data used are a subset of the Historical Climatology Network (1036 stations) and a stream gage network of 1009 stations. Trend significance was determined using the nonparametric seasonal Kendall's test on a monthly and annual basis, and a robust slope estimator was used for determination of trend magnitudes. A bivariate test was used for evaluation of relative changes in the variables, specifically, streamflow relative to precipitation, streamflow relative to temperature, and precipitation relative to temperature.

Strong trends were found in all of the variables at many more stations than would be expected due to chance. There is a strong spatial and seasonal structure in the trend results. For instance, although annual temperature increases were found at many stations, mostly in the North and West, there were almost as many downtrends, especially in the South and East. Among the most important trend patterns are (a) increases in March temperature at almost half of the stations; (b) increases in precipitation from September through December at as many as 25 percent of the stations, mostly in the central part of the country; (c) strong increases in streamflow in the period November–April at a maximum of almost half of the stations, with the largest trend magnitudes in the north-central states; (d) changes in the temperature range (mostly downward) at a large number of stations beginning in late spring and continuing through winter, affecting as many as over half of the stations. The observed trends in streamflow are not entirely consistent with the changes in the climatic variables and may be due to a combination of climatic and water management effects.

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P. R. Field, A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison

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Identical composite analysis of midlatitude cyclones over oceanic regions has been carried out on both output from the NCAR Community Atmosphere Model, version 3 (CAM3) and multisensor satellite data. By focusing on mean fields associated with a single phenomenon, the ability of the CAM3 to reproduce realistic midlatitude cyclones is critically appraised. A number of perturbations to the control model were tested against observations, including a candidate new microphysics package for the CAM. The new microphysics removes the temperature-dependent phase determination of the old scheme and introduces representations of microphysical processes to convert from one phase to another and from cloud to precipitation species. By subsampling composite cyclones based on systemwide mean strength (mean wind speed) and systemwide mean moisture the authors believe they are able to make meaningful like-with-like comparisons between observations and model output. All variations of the CAM tested overestimate the optical thickness of high-topped clouds in regions of precipitation. Over a system as a whole, the model can both over- and underestimate total high-topped cloud amounts. However, systemwide mean rainfall rates and composite structure appear to be in broad agreement with satellite estimates. When cyclone strength is taken into account, changes in moisture and rainfall rates from both satellite-derived observations and model output as a function of changes in sea surface temperature are in accordance with the Clausius–Clapeyron equation. The authors find that the proposed new microphysics package shows improvement to composite liquid water path fields and cloud amounts.

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R. B. Thorpe, J. M. Gregory, T. C. Johns, R. A. Wood, and J. F. B. Mitchell

Abstract

Models of the North Atlantic thermohaline circulation (THC) show a range of responses to the high-latitude warming and freshening characteristic of global warming scenarios. Most simulate a weakening of the THC, with some suggesting possible interruption of the circulation, but others exhibit little change. The mechanisms of the THC response to climate change using the HadCM3 coupled ocean–atmosphere general circulation model, which gives a good simulation of the present-day THC and does not require flux adjustment, were studied. In a range of climate change simulations, the strength of the THC in HadCM3 is proportional to the meridional gradient of steric height (equivalent to column-integrated density) between 30°S and 60°N. During an integration in which CO2 increases at 2% per year for 70 yr, the THC weakens by about 20%, and it stabilizes at this level if the CO2 is subsequently held constant. Changes in surface heat and water fluxes are the cause of the reduction in the steric height gradient that derives the THC weakening, 60% being due to temperature change (greater warming at high latitudes) and 40% to salinity change (decreasing at high latitude, increasing at low latitude). The level at which the THC stabilizes is determined by advective feedbacks. As the circulation slows down, less heat is advected northward, which counteracts the in situ warming. At the same time, northward salinity advection increases because of a strong increase in salinity in the subtropical Atlantic, due to a greater atmospheric export of freshwater from the Atlantic to the Pacific. This change in interbasin transport means that salinity effects stabilize the circulation, in contrast to a single basin model of the THC, where salinity effects are destabilizing. These results suggest that the response of the Atlantic THC to anthropogenic forcing may be partly determined by events occurring outside the Atlantic basin.

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Matthew B. Menary, Daniel L. R. Hodson, Jon I. Robson, Rowan T. Sutton, and Richard A. Wood

Abstract

The North Atlantic Ocean subpolar gyre (NA SPG) is an important region for initializing decadal climate forecasts. Climate model simulations and paleoclimate reconstructions have indicated that this region could also exhibit large, internally generated variability on decadal time scales. Understanding these modes of variability, their consistency across models, and the conditions in which they exist is clearly important for improving the skill of decadal predictions—particularly when these predictions are made with the same underlying climate models. This study describes and analyzes a mode of internal variability in the NA SPG in a state-of-the-art, high-resolution, coupled climate model. This mode has a period of 17 yr and explains 15%–30% of the annual variance in related ocean indices. It arises because of the advection of heat content anomalies around the NA SPG. Anomalous circulation drives the variability in the southern half of the NA SPG, while mean circulation and anomalous temperatures are important in the northern half. A negative feedback between Labrador Sea temperatures/densities and those in the North Atlantic Current (NAC) is identified, which allows for the phase reversal. The atmosphere is found to act as a positive feedback on this mode via the North Atlantic Oscillation (NAO), which itself exhibits a spectral peak at 17 yr. Decadal ocean density changes associated with this mode are driven by variations in temperature rather than salinity—a point which models often disagree on and which may affect the veracity of the underlying assumptions of anomaly-assimilating decadal prediction methodologies.

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