The seasonal cycle and the business cycle

Business cycles

Performance of combined double seasonal univariate time series models for forecasting water demand

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle and a within-year seasonal cycle are accommodated in the various model specifications to capture both seasonalities. We investigate whether combining forecasts from different methods for different origins and horizons could improve forecast accuracy. The analysis is made with daily data for water consumption in Granada, Spain.ARIMA, Combined forecasts, Double seasonality, Exponential Smoothing, Forecasting, GARCH, Water demand

Statistics of the seasonal cycle of the 1951-2000 surface temperature records in Italy

We present an analysis of seasonal cycle of the last 50 years of records of surface temperature in Italy. We consider two data sets which synthesize the surface temperature fields of Northern and Southern Italy. Such data sets consist of records of daily maximum and minimum temperature. We compute the best estimate of the seasonal cycle of the variables considered by adopting the cyclograms' technique. We observe that in general the minimum temperature cycle lags behind the maximum temperature cycle, and that the cycles of the Southern Italy temperatures records lag behind the corresponding cycles referring to Northern Italy. All seasonal cycles lag considerably behind the solar cycle. The amplitude and phase of the seasonal cycles do not show any statistically significant trend in the time interval considered.Comment: 30 pages, 6 figures, submitted to IJ

On the seasonal variability of the Canary Current and the Atlantic Meridional Overturning Circulation

Author Posting. © American Geophysical Union, 2017. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 122 (2017): 4518–4538, doi:10.1002/2017JC012774.The Atlantic Meridional Overturning Circulation (AMOC) is continually monitored along 26°N by the RAPID-MOCHA array. Measurements from this array show a 6.7 Sv seasonal cycle for the AMOC, with a 5.9 Sv contribution from the upper mid-ocean. Recent studies argue that the dynamics of the eastern Atlantic is the main driver for this seasonal cycle; specifically, Rossby waves excited south of the Canary Islands. Using inverse modeling, hydrographic, mooring, and altimetry data, we describe the seasonal cycle of the ocean mass transport around the Canary Islands and at the eastern boundary, under the influence of the African slope, where eastern component of the RAPID-MOCHA array is situated. We find a seasonal cycle of −4.1 ± 0.5 Sv for the oceanic region of the Canary Current, and +3.7 ± 0.4 Sv at the eastern boundary. This seasonal cycle along the eastern boundary is in agreement with the seasonal cycle of the AMOC that requires the lowest contribution to the transport in the upper mid-ocean to occur in fall. However, we demonstrate that the linear Rossby wave model used previously to explain the seasonal cycle of the AMOC is not robust, since it is extremely sensitive to the choice of the zonal range of the wind stress curl and produces the same results with a Rossby wave speed of zero. We demonstrate that the seasonal cycle of the eastern boundary is due to the recirculation of the Canary Current and to the seasonal cycle of the poleward flow that characterizes the eastern boundaries of the oceans.RAPROCAN Project ; Instituto Español de Oceanografía; and as part of the SeVaCan project Grant Number: CTM2013-48695; Ministerio de Economía y Competividad; Apoyo al Personal Investigador en Formación2017-12-0

Radiative Effects of Stratospheric Seasonal Cycles in the Tropical Upper Troposphere and Lower Stratosphere

Water vapor and ozone are powerful radiative constituents in the tropical lower stratosphere, impacting the local heating budget and nonlocally forcing the troposphere below. Their near-tropopause seasonal cycle structures imply associated "radiative seasonal cycles" in heating rates that could affect the amplitude and phase of the local temperature seasonal cycle. Overlying stratospheric seasonal cycles of water vapor and ozone could also play a role in the lower stratosphere and upper troposphere heat budgets through nonlocal propagation of radiation. Previous studies suggest that the tropical lower stratospheric ozone seasonal cycle radiatively amplifies the local temperature seasonal cycle by up to 35%, while water vapor is thought to have a damping effect an order of magnitude smaller. This study uses Aura Microwave Limb Sounder observations and an offline radiative transfer model to examine ozone, water vapor, and temperature seasonal cycles and their radiative linkages in the lower stratosphere and upper troposphere. Radiative sensitivities to ozone and water vapor vertical structures are explicitly calculated, which has not been previously done in a seasonal cycle context. Results show that the water vapor radiative seasonal cycle in the lower stratosphere is not sensitive to the overlying water vapor structure. In contrast, about one-third of ozone's radiative seasonal cycle amplitude at 85 hPa is associated with longwave emission above 85 hPa. Ozone's radiative effects are not spatially homogenous: for example, the Northern Hemisphere tropics have a seasonal cycle of radiative temperature adjustments with an amplitude 0.8 K larger than the Southern Hemisphere tropics. Keywords: Stratosphere; Tropopause; Ozone; Radiation budgets; Water vapor; Seasonal cycleNational Science Foundation (U.S.) (Grant AGS-1461517)United States. National Aeronautics and Space Administration. Earth and Space Science Fellowship Program (Grant NNX14AK83H

Seasonal cycle of precipitation variability in South America on intraseasonal timescales

The seasonal cycle of the intraseasonal (IS) variability of precipitation in South America is described through the analysis of bandpass filtered outgoing longwave radiation (OLR) anomalies. The analysis is discriminated between short (10--30 days) and long (30--90 days) intraseasonal timescales. The seasonal cycle of the 30--90-day IS variability can be well described by the activity of first leading pattern (EOF1) computed separately for the wet season (October--April) and the dry season (May--September). In agreement with previous works, the EOF1 spatial distribution during the wet season is that of a dipole with centers of actions in the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), while during the dry season, only the last center is discernible. In both seasons, the pattern is highly influenced by the activity of the Madden--Julian Oscillation (MJO). Moreover, EOF1 is related with a tropical zonal-wavenumber-1 structure superposed with coherent wave trains extended along the South Pacific during the wet season, while during the dry season the wavenumber-1 structure is not observed. The 10--30-day IS variability of OLR in South America can be well represented by the activity of the EOF1 computed through considering all seasons together, a dipole but with the stronger center located over SESA. While the convection activity at the tropical band does not seem to influence its activity, there are evidences that the atmospheric variability at subtropical-extratropical regions might have a role. Subpolar wavetrains are observed in the Pacific throughout the year and less intense during DJF, while a path of wave energy dispersion along a subtropical wavetrain also characterizes the other seasons. Further work is needed to identify the sources of the 10--30-day-IS variability in South America

The seasonal cycle of N_2O

We have carried out an empirical study of the seasonal cycle of nitrous oxide (N_2O) using the data archived by the National Oceanic and Atmospheric Administration – Climate Monitoring and Diagnostics Laboratory (NOAA-CMDL) Global Cooperative Air Sampling Network from 1977 to 2000. In order to isolate the seasonal cycle, we first detrended the data using least square polynomial fits. The remaining variability was averaged to extract the seasonal cycle, which has an amplitude of about 0.8 ppbv. The statistical significance of the seasonal signal was established using the multitaper method and Welch's method for power spectrum analysis

Regional scale characteristics of the seasonal cycle of chlorophyll in the Southern Ocean

In the Ocean, the seasonal cycle is the mode that couples climate forcing to ecosystem response in production, diversity and carbon export. A better characterisation of the ecosystem's seasonal cycle therefore addresses an important gap in our ability to estimate the sensitivity of the biological pump to climate change. In this study, the regional characteristics of the seasonal cycle of phytoplankton biomass in the Southern Ocean are examined in terms of the timing of the bloom initiation, its amplitude, regional scale variability and the importance of the climatological seasonal cycle in explaining the overall variance. The seasonal cycle was consequently defined into four broad zonal regions; the subtropical zone (STZ), the transition zone (TZ), the Antarctic circumpolar zone (ACZ) and the marginal ice zone (MIZ). Defining the Southern Ocean according to the characteristics of its seasonal cycle provides a more dynamic understanding of ocean productivity based on underlying physical drivers rather than climatological biomass. The response of the biology to the underlying physics of the different seasonal zones resulted in an additional classification of four regions based on the extent of inter-annual seasonal phase locking and the magnitude of the integrated seasonal biomass. This regionalisation contributes towards an improved understanding of the regional differences in the sensitivity of the Southern Oceans ecosystem to climate forcing, potentially allowing more robust predictions of the effects of long term climate trends