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William J. Merryfield

Abstract

An EOF analysis is used to intercompare the response of ENSO-like variability to CO2 doubling in results from 15 coupled climate models assembled for the Intergovernmental Panel on Climate Change Fourth Assessment Report. Under preindustrial conditions, 12 of the 15 models exhibit ENSO amplitudes comparable to or exceeding that observed in the second half of the twentieth century. Under CO2 doubling, three of the models exhibit statistically significant (p < 0.1) increases in ENSO amplitude, and five exhibit significant decreases. The overall amplitude changes are not strongly related to the magnitude or pattern of surface warming. It is, however, found that ENSO amplitude decreases (increases) in models having a narrow (wide) ENSO zonal wind stress response and ENSO amplitude comparable to or greater than observed. The models exhibit a mean fractional decrease in ENSO period of about 5%. Although many factors can influence the ENSO period, it is suggested that this may be related to a comparable increase in equatorial wave speed through an associated speedup of delayed-oscillator feedback. Changes in leading EOF, characterized in many of the models by a relative increase in the amplitude of SST variations in the central Pacific, are in most cases consistent with effects of anomalous zonal and vertical advection resulting from warming-induced changes in SST.

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William J. Merryfield
and
George J. Boer

Abstract

Variability of subtropical cell (STC) overturning in the upper Pacific Ocean is examined in a coupled climate model in light of large observed changes in STC transport. In a 1000-yr control run, modeled STC variations are smaller than observed, but correlate in a similar way with low-frequency ENSO-like variability. In model runs that include anthropogenically forced climate change, STC pycnocline transports decrease progressively under the influence of global warming, attaining reductions of 8% by 2000 and 46% by 2100. Although the former reduction is insufficient to fully account for the apparent observed decline in STC transport over recent decades, it does suggest that global warming may have contributed to the observed changes. Analysis of coupled model results shows that STC transports play a significant role in modulating tropical Pacific Ocean heat content, and that such changes are dominated by anomalous currents advecting mean temperature, rather than by advection of temperature anomalies by mean currents.

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Reinel Sospedra-Alfonso
and
William J. Merryfield

Abstract

This study examines the changing roles of temperature and precipitation on snowpack variability in the Northern Hemisphere for Second Generation Canadian Earth System Model (CanESM2) historical (1850–2005) and future (2006–2100) climate simulations. The strength of the linear relationship between monthly snow water equivalent (SWE) in January–April and precipitation P or temperature T predictors is found to be a sigmoidal function of the mean temperature over the snow season up to the indicated month. For P predictors, the strength of this relationship increases for colder snow seasons, whereas for T predictors it increases for warmer snow seasons. These behaviors are largely explained by the daily temperature percentiles below freezing during the snow accumulation period. It is found that there is a threshold temperature (−5±1°C, depending on month in the snow season and largely independent of emission scenario), representing a crossover point below which snow seasons are sufficiently cold that P is the primary driver of snowpack amount and above which T is the primary driver. This isotherm allows one to delineate the snow-climate regions and elevation zones in which snow-cover amounts are more vulnerable to a warming climate. As climate projections indicate that seasonal isotherms shift northward and toward higher elevations, regions where snowpack amount is mainly driven by precipitation recede, whereas temperature-sensitive snow-covered areas extend to higher latitudes and/or elevations, with resulting impacts on ecosystems and society.

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Reinel Sospedra-Alfonso
and
William J. Merryfield

Abstract

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

Open access
Arlan Dirkson
,
William J. Merryfield
, and
Adam Monahan

Abstract

A promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with SIT-IMs that best represent both processes. Similar skill to that achieved by initializing with PIOMAS, including skillful predictions of detrended September SIA from May, is obtained by initializing with two of the statistical models. Regional skill for September SIC is also enhanced using improved SIT-IMs, with an increase in the spatial coverage of statistically significant skill from 10% to 60%–70% of the appreciably varying ice pack. Reduced skill is seen, however, in the Nordic seas using the improved SIT-IMs, resulting from an inherent cold sea surface temperature bias in CanCM3 that is amplified by a thicker initial ice cover.

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Fabian Lienert
,
John C. Fyfe
, and
William J. Merryfield

Abstract

This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.

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Robert Fajber
,
Adam H. Monahan
, and
William J. Merryfield

Abstract

The timing of daily extreme wind speeds from 10 to 200 m is considered using 11 yr of 10-min averaged data from the 213-m tower at Cabauw, the Netherlands. This analysis is complicated by the tendency of autocorrelated time series to take their extreme values near the beginning or end of a fixed window in time, even when the series is stationary. It is demonstrated that a simple averaging procedure using different base times to define the day effectively suppresses this “edge effect” and enhances the intrinsic nonstationarity associated with diurnal variations in boundary layer processes. It is found that daily extreme wind speeds at 10 m are most likely in the early afternoon, whereas those at 200 m are most likely in between midnight and sunrise. An analysis of the joint distribution of the timing of extremes at these two altitudes indicates the presence of two regimes: one in which the timing is synchronized between these two layers, and the other in which the occurrence of extremes is asynchronous. These results are interpreted physically using an idealized mechanistic model of the surface layer momentum budget.

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Arlan Dirkson
,
William J. Merryfield
, and
Adam H. Monahan

Abstract

Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-based probabilistic forecasts of local sea ice concentration (SIC). The first of these improvements is the application of the parametric zero- and one-inflated beta (BEINF) probability distribution, suitable for doubly bounded variables such as SIC, for obtaining a smoothed forecast probability distribution. The second improvement is the introduction of a novel calibration technique, termed trend-adjusted quantile mapping (TAQM), that explicitly takes into account SIC trends and is applied using the BEINF distribution. We demonstrate these methods using a set of 10-member ensemble SIC hindcasts from the Third Generation Canadian Climate Coupled Global Climate Model (CanCM3) over the period 1981–2017. Though fitting ensemble SIC hindcasts to the BEINF distribution consistently improves probabilistic hindcast skill relative to a simpler “count based” probability approach in perfect model experiments, it does not itself correct model biases that may reduce this improvement when verifying against observations. The TAQM calibration technique is effective at removing SIC biases present in CanCM3 and improving forecast reliability. Over the recent 2000–17 period, TAQM-calibrated SIC hindcasts show improved skill relative to uncalibrated hindcasts. Compared against a climatological reference forecast adjusted for the trend, TAQM-calibrated hindcasts show widespread skill, particularly in September, even at 3–4-month lead times.

Open access
R. S. Ajayamohan
,
William J. Merryfield
, and
Viatcheslav V. Kharin

Abstract

The nature of the increasing frequency of extreme rainfall events (ERE) in central India is investigated by relating their occurrence to synoptic activity. Using a long record of the paths and intensities of monsoon synoptic disturbances, a synoptic activity index (SAI) is defined whose interannual variation correlates strongly with that in the number of ERE, demonstrating a strong connection between these phenomena. SAI furthermore shows a rising trend that is statistically indistinguishable from that in ERE, indicating that the increasing frequency of ERE is likely attributable to a rising trend in synoptic activity. This synoptic activity increase results from a rising trend in relatively weak low pressure systems (LPS), and it outweighs a declining trend in stronger LPS.

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Hai Lin
,
Ryan Muncaster
,
Jacques Derome
,
William J. Merryfield
, and
Gulilat Diro

Abstract

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Inter-annual Prediction System version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than six months in advance in several middle latitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper tropospheric circum-global wave train. Although a large part of the forecast skill for the surface air temperature and 500 hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO related SST anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western United States regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for further development of a seasonal forecasting system.

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