# Investigation of the Dominant Factors Influencing the ERA15 Temperature Increments at the Subtropical and Temperate Belts with a Focus over the Eastern Mediterranean Region

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Research Area and Data

#### 2.1. Research Area

#### 2.2. Land Use Change Data

**Figure 1.**The research area (GLOB, 180° × 30°) and subregions. AC, Asian continental (80° × 10°), AHT; Asian high terrain (45° × 25°); EM, Eastern Mediterranean (12° × 4°); EWA, Europe and West Asia (60° × 10°); IS, Israel (8° × 4°) and the vicinity; SH, Sahara Desert (40° × 20°)

*****.

*****The sensitivity to the exact definition of the boundaries of each subdomain was not tested, because most of the subdomains include a very large number of points, and it was assumed to be insensitive to their exact boundary locations.

#### 2.3. ERA15 and the Temperature Increments

#### 2.4. Other Independent Factors

**Table 1.**The different factors employed in the different regression models. Factors 1–7 serve as predictors (independent), while 8, the IAU (T), serves as the predicted variable (dependent). MAM, March, April and May; JJA, June, July and August.

No. | Factor | Description (units) |
---|---|---|

1 | LUCI | Land Use Change Index (index) |

2 | NDVI _{x} | Where x is a seasonal average of MAM and JJA (NDVI/day) |

3 | TOMS | TOMS-AI (index) |

4 | OMEGA | Vertical-p velocity omega (OMEGA) at 500 hPa (hPa∙d^{−1}) |

5 | TOPO | Topography (m) |

6 | POPU | Population density with natural log transformation (person/km^{2}) |

7 | SEIS | The seismic hazard assessment (%) |

8 | IAU(T) | Increment analysis update of temperature (K/day) |

## 3. Main Research Goals

## 4. Methodology

#### Different Multi-Regression Runs

- (1)
- Dummy variable run (the basic run, as shown at Table 2 for the seismic hazard factor)

**Table 2.**Summary of multiple regression results of the dummy variable run, the “basic run” for the different research regions (full and subdomains) predicting Incremental Analysis Updates for the month of May (MAY-IAU) (T) (y). +: positive association; –: negative association. The variable abbreviations are described in Table 1. Predictors are given as a regression model equation in order of importance. All coefficients are significant at p < 0.05. All abbreviations are defined in Table 1.

Model + Variables Entered (x_{1,} x_{2,} x_{3,} x_{4,} _{…}_{,} x_{n}) | R^{2} | N | Regression Run | Pressure Level |
---|---|---|---|---|

y = 0.55TOPO + 0.21POPU + 0.08SEIS + 0.02TOMS + 0.01NDVI∙MAM | 0.4 | 22,021 | Basic-GLOB | 500 |

y = 0.68TOPO + 0.05SEIS − 0.06NDVI∙MAM + 0.03POPU − 0.02TOMS | 0.53 | 22,021 | Basic-GLOB | 700 |

y = 0.37TOPO + 0.29TOMS + 0.11SEIS + 0.04NDVI∙MAM − 0.04OMEGA + 0.03POPU | 0.28 | 22,021 | Basic-GLOB | 850 |

y = 0.66TOMS + 0.18NDVI∙MAM +0.09POPU − 0.03OMEGA + 0.02TOPO − 0.02SEIS | 0.43 | 22,021 | Basic-GLOB | 1000 |

y = 0.305SEIS − 0.156OMEGA + 0.112POPU + 0.038TOMS + 0.026NDVI∙MAM | 22,021 | Omitting Topo | 500 | |

y = 0.340SEIS − 0.183OMEGA − 0.101POPU − 0.044NDVI∙MAM | 22,021 | Omitting Topo | 700 | |

y = 0.259SEIS + 0.303TOMS − 0.141OMEGA + 0.045NDVI∙MAM − 0.034POPU | 22,021 | Omitting Topo | 850 | |

y = 0.664TOMS + 0.178NDVI∙MAM + 0.087POPU − 0.029OMEGA | 22,021 | Omitting Topo | 1000 | |

y = 0.18TOPO + 0.19TOMS + 0.13OMEGA + 0.07SEIS + 0.06NDVI∙MAM − 0.05POPU | 0.19 | 3381 | Basic-AC | 500 |

y = 0.71TOPO − 0.13NDVI∙MAM + 0.19TOMS − 0.12POPU + 0.06 SEIS + 0.07OMEGA | 0.51 | 3381 | Basic-AC | 700 |

y = 0.74TOPO + 0.71TOMS + 0.43OMEGA − 0.31POPU + 0.06NDVI∙MAM | 0.47 | 3381 | Basic-AC | 850 |

y = −0.39POPU + 0.72TOMS + 0.51OMEGA + 0.08TOPO + 0.03SEIS | 0.43 | 3381 | Basic-AC | 1000 |

y = 0.61TOPO + 0.32TOMS + 0.13POPU − 0.06OMEGA + 0.03SEIS | 0.33 | 6681 | Basic-AHT | 500 |

y = 0.65TOPO − 0.29POPU + 0.22TOMS − 0.08OMEGA | 0.49 | 6681 | Basic-AHT | 700 |

y = −0.50POPU − 0.17TOPO + 0.18OMEGA − 0.14NDVI∙MAM + 0.14TOMS + 0.07SEIS | 0.29 | 6681 | Basic-AHT | 850 |

y = 0.37TOMS − 0.28TOPO − 0.19POPU + 0.13OMEGA − 0.10NDVI∙MAM − 0.03SEIS | 0.67 | 6681 | Basic-AHT | 1000 |

y = −0.20OMEGA − 0.13 SEIS + 0.11TOPO + 0.01NDVI∙MAM | 0.23 | 2541 | Basic-EWA | 500 |

y = −0.16TOPO − 0.02NDVI∙MAM − 0.10OMEGA + 0.09TOMS − 0.02POPU | 0.15 | 2541 | Basic-EWA | 700 |

y = 0.73TOPO − 0.26OMEGA + 0.22TOMS − 0.04POPU | 0.48 | 2541 | Basic-EWA | 850 |

y = 0.19NDVI∙MAM + 0.76TOMS − 0.13SEIS + 0.20TOPO + 0.07POPU + 0.09OMEGA | 0.27 | 2541 | Basic-EWA | 1000 |

y = 0.14POPU − 0.06NDVI∙MAM + 0.06TOMS − 0.08TOPO + 0.14SEIS + 0.04OMEGA | 0.12 | 3321 | Basic-SH | 500 |

y = −0.21NDVI∙MAM − 0.06TOMS − 0.06OMEGA + 0.14SEIS | 0.14 | 3321 | Basic-SH | 700 |

y = 2.54TOPO + 0.29NDVI∙MAM − 0.32SEIS − 0.08OMEGA | 0.7 | 3321 | Basic-SH | 850 |

y = 0.76TOPO + 0.54TOMS + 0.34NDVI∙MAM − 0.29OMEGA + 0.19POPU | 0.63 | 3321 | Basic-SH | 1000 |

y = −0.32TOMS − 0.17OMEGA + 0.16NDVI∙MAM | 0.52 | 225 | Basic-EM | 500 |

y = −0.63TOMS + 0.05 SEIS | 0.51 | 225 | Basic-EM | 700 |

y = −0.33TOMS + 0.12OMEGA + 0.16NDVI∙MAM | 0.4 | 225 | Basic-EM | 850 |

y = 1.14TOMS + 0.20SEIS − 0.39OMEGA + 0.33NDVI∙MAM | 0.7 | 225 | Basic-EM | 1000 |

y = 0.11SEIS − 0.18TOMS + 0.10OMEGA + 0.22TOPO + 0.06POPU − 0.08NDVI∙MAM | 0.53 | 153 | Basic-IS | 500 |

y = 0.16NDVI∙MAM − 0.27TOMS − 0.45TOPO − 0.07OMEGA + 0.10POPU − 0.11SEIS | 0.6 | 153 | Basic-IS | 700 |

y = −0.33OMEGA + 0.98TOPO − 0.31TOMS − 0.11SEIS | 0.75 | 153 | Basic-IS | 850 |

y = 1.73TOPO + 0.83TOMS − 0.24OMEGA + 0.33SEIS | 0.8 | 153 | Basic-IS | 1000 |

- (2)
- The “Longitude and Latitude run”

- (3)
- The ”No-Topography run”

**Figure 2.**Flow chart of the multi-regression procedure; Y is the dependent variable (ERA15 increments), which can be expressed in terms of a constant (a) and slopes (b) times each of the independent X variables (TOMS, omega, topography, seismic hazard (marked with orange for being the dummy variable), population density and land use data). The constant is also referred to as the intercept, and the slope as the regression coefficient or b coefficient.

## 5. Analysis of the Regression Model Results: Preliminary Results

- GLOB full domain: topography at 700 hPa and TOMS-AI at 1000 hPa (22,021).
- Eastern Mediterranean:
- East Mediterranean (EM): TOMS-AI and weak effect of NDVI JJA at 1000 hPa (225).
- Israel (IS): TOMS-AI at 700 hPa and NDVI JJA at 1000 hPa (153).

- Asia Continental (AC): topography at 700 hPa and TOMS-AI at 1000 hPa (3381).
- Asia High Terrain (AHT): topography and TOMS-AI at 700 hPa (6681).
- Europe West Asia (EWA): TOMS-AI at 1000 and 850 hPa, topography at 850 hPa and NDVI in the JJA months at 1000 hPa (2541).
- Sahara (SH): topography at 850 and 1000 hPa (3321).

^{2}at 500 and 850 hPa for the GLOB (A) and for all the sub-regions; EM (B), IS (C), SH (D), AC (E), AHT (F) and EWA (G).

**Table 3.**Qualitative summary of the multi-regression results for the different research regions. Bold: absolute dominant variable in the annual course. The yellow marking indicates a negative coefficient value.

Level (hPa) | Annual Range of R^{2} | Factors in Priority (Top 3) | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Region | GLOB | AC | AHT | EWA | SH | EM | IS | GLOB | AC | AHT | EWA | SH | EM | IS |

1000 | 0.24 0.42 | 0.02 0.24 | 0.08 0.3 | 0.11 0. 38 | 0.01 0.21 | 0.02 0.37 | 0.01 0.12 | TOMS | TOMS | TOMS | TOMS | Topo | TOMS | Topo |

Topo | Omega | Topo | Topo | TOMS | NDVI | TOMS | ||||||||

Popu | Topo/Popu | Popu | NDVI/Omega | NDVI/Popu | Seis | Omega/NDVI | ||||||||

850 | 0.19 0.39 | 0.09 0.24 | 0.01 0.31 | 0.12 0.34 | 0.08 0.38 | 0.01 0.05 | 0.01 0.32 | Topo | Topo | Topo | Topo | Topo | NDVI | Topo |

TOMS | TOMS | Popu | TOMS | NDVI | TOMS | NDVI | ||||||||

Omega | Omega | Omega | Omega | Seis | Omega | |||||||||

700 | 0.27 0.55 | 0.01 0.45 | 0.20 0.51 | 0.01 0.32 | 0.02 0.42 | 0.00 0.06 | 0.01 0.33 | Topo | Topo | Topo | Omega | Topo | TOMS | Topo |

TOMS | TOMS | TOMS | Topo | NDVI | NDVI | TOMS | ||||||||

Seismic | Omega | Omega | TOMS | |||||||||||

500 | 0.29 0.40 | 0.04 0.26 | 0.18 0.34 | 0.01 0.11 | 0.01 0.11 | 0.01 0.16 | 0.01 0.24 | Topo | TOMS | Topo | Omega | Omega | TOMS | TOMS |

TOMS/Popu | Omega | Omega | TOMS | TOMS | Omega | Seis | ||||||||

Omega | Topo/Popu | TOMS | NDVI | NDVI |

**Figure 3.**The annual course of the multi-regression R

^{2}values at the different pressure levels at the full research region (

**A**, GLOB) and at each subregion (

**B**–

**G**).

## 6. “Small Cell” Analysis

^{2}> 0.000175).

^{2}). As previously noted, R

^{2}is one of the major statistical parameters to test the quality and success of the multiple regression predictors to predict the dependent variable. Table 4 summarizes the R

^{2}monthly variation of the current regression runs at the different pressure levels.

**Table 4.**Annual categorization of R

^{2}values for the 4° by 4°-cell regression run, at the different pressure levels. Each cell consists of 81 grid points (N = 81), and values are significant at the p < 0.05 level.

Pressure Level (hPa) | Jane | February | March | April | May | June | July | August | Septemper | October | November | December |
---|---|---|---|---|---|---|---|---|---|---|---|---|

500 | L | L | L | L | L | L | L | L | L | L | L | L |

700 | L | L | VH | VH | VH | VH | H | H | H | H | M | L |

850 | L | L | M | L | L | VL | VL | VL | VL | L | L | L |

1000 | L | L | L | L | M | M | L | L | L | L | L | L |

^{2}

_{4}

_{×}

_{4}value > 0.45; high ( ), R

^{2}

_{4}

_{×}

_{4}value 0.4–0.45; moderate ( ), R

^{2}

_{4}

_{×}

_{4}value 0.35–0.4; low ( ), R

^{2}

_{4}

_{×}

_{4}value 0.15–0.3; very low ( )—R

^{2}

_{4}

_{×}

_{4}value < 0.15.

^{2}

_{4}

_{×}

_{4}> 0.4. High values of R

^{2}

_{4}

_{×}

_{4}> 0.45 were obtained for spring months (March–June).

^{2}

_{4}

_{×}

_{4}< 0.15, which represent very low values in the summer months (June–September). The rest of the months represent low values, except March, with moderate values (0.35 < R

^{2}

_{4}

_{×}

_{4}< 0.4).

## 7. Analyzing the Spatial Distribution of the Multi-Regression Coefficients of the Different Variables

#### 7.1. Spatial “Small Cell” Analysis

- The combination of the R
^{2}_{4}_{×}_{4}and regression coefficients of the different factors at 700 hPa during March–June demonstrates the importance of the NDVI trends and the seismic hazard assessment factor. The latter variable shows up in spite of being chosen as the “dummy variable”, probably due to its close relation with topography. - The following regions have demonstrated the best multiple regression correlations to explain the IAU (T); the Atlas Mountains (especially the eastern slope), the Sahara Desert (Ahaggar Mountains), the Nile Valley and Delta, the Euphrates Valley, the Persian Gulf coast of Saudi Arabia, the Aral Sea and its vicinity and the BoHai Bay vicinity (West of Beijing). This fact is especially true at 700 hPa for the period of March–June, as noted earlier (Figure 4).

**Figure 4.**Map of zonal and meridional vertical cross-sections of R

^{2}

_{4}

_{×}

_{4}values with zooming on May. All values are significant at the p < 0.05 level. Missing values (white spots) represent R

^{2}

_{4}

_{×}

_{4}values that are insignificant.

#### 7.2. Vertical Analysis of Layers and Factors

- The dominant level in explaining the IAU (T) at each step of the analyses is 700 hPa. The “small cell” analysis strengthens this finding by identifying the period of months with the highest explanation of IAU (T), which is March to June. The important role of the 700-hPa level in explaining the IAU (T) is probably due to the fact that this level represents both upper atmospheric processes and is still being affected by land-atmospheric interaction processes, avoiding the larger “noise” of these influences typical for the 850 hPa. Hence, the 700-hPa level seems to “filter” the noise from the boundary-layer, as compared to 850 hPa.
- The 1000-hPa level is second in contributing to the IAU (T) (Table 5) The 850-hPa level demonstrated the weakest relation and contribution to the temperature increments.
- The 700-hPa level demonstrates the best explanation for the ERA15 temperature increments in the “small cell” analysis. The vertical cross-section (Figure 4) also supports this result.
- The topography coefficients are highest at 850 hPa and 700 hPa, which fit the high terrain of the region. These levels are the most favorable in representing the topography factor in the regression equations due to the averaged terrain height, which often reaches their altitudes.
- As expected, the TOMS-AI coefficients tend to show a decrease with height, although the starting point in the vertical varies; at most points, it starts at the lowest level of 1000 hPa. The coefficients occupy all pressure levels that were studied, mainly 1000 and 850 hPa, but also can reach up the 500 hPa. It also depends on the distance from the aerosols’ sources.
- The vertical profiles for the population density multiple regression coefficients show no specific trend with height. Although it was expected to have its maximum near the ground level, the stronger factors, such as TOMS-AI and “NDVI trends”, which have also a close relation to the ground level, become dominant in the model errors. It is noticeable, however, that the highest pressure level being reached by the population density coefficients is strongly related to the respective population density in the region. In other words, the highest population density regions do show the strongest vertical influence on temperature increments.
- The “NDVI trends” factor is present at each of the profile points and demonstrates, as expected, a clear dependency with height, i.e., the coefficients’ values decrease with height. The NDVI factor is best represented at 1000 and 850 hPa, but can be found also at the higher levels of 700 and 500 hPa.
- The omega 500 factor vertical analysis has no clear trend in the vertical direction. There are vertical changes at different geographic locations. For instance, the marine vicinity is characterized by significant coefficients of omega 500 at the lower levels of the atmosphere, while at continental locations, this factor enters at higher levels. The latter finding can be attributed to the interaction of the synoptic state with sea-land breeze, which is dominant in coastal regions and, due to its “small scale” or local effect, is not being represented well by the model resolution. Moreover, due to a lack of a clear trend in its influence on the IAU (T), it seems that the omega 500 factor fails to represent the synoptic state well. Perhaps this is due to the fact that monthly averages are smoothing the daily time scale, which is important for synoptic systems.
- The seismic hazard assessment regression coefficients decrease with height at points where the coefficients show a clear vertical structure. Their entrance into the regression equations is quite compatible with the spatial distribution of the factor itself.

**Table 5.**The parameter (or factor) prioritization for the GLOB and for each subdomain. The colors represent the three dominant parameters: gold, the most influential parameter; silver, the second; bronze, the third. The months in which these effects are taking place are in brackets.

Area | GLOB | AC | AHT | EWA | SH | EM | IS | |
---|---|---|---|---|---|---|---|---|

Level (hPa) | ||||||||

500 | ||||||||

700 | TOPO | TOPO (4–9) | TOPO (11–5), | TOPO | TOMS-AI (4) | |||

* TOMS-AI (11–3) | ||||||||

850 | * TOMS-AI (12–3) | TOPO | NDVI MAM | |||||

TOPO (4–9) | NDVI MAM (4) | |||||||

1000 | TOMS-AI (5–10) | TOMS-AI (5–9) | TOMS-AI (3–6) | TOMS-AI (4–5,11) | ||||

NDVI JJA (5–9) | * NDVI JJA (4–5,11) |

## 8. Factors Analysis: Focusing on the Eastern Mediterranean Region

## 9. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Hirsch-Eshkol, T.; Baharad, A.; Alpert, P.
Investigation of the Dominant Factors Influencing the ERA15 Temperature Increments at the Subtropical and Temperate Belts with a Focus over the Eastern Mediterranean Region. *Land* **2014**, *3*, 1015-1036.
https://doi.org/10.3390/land3031015

**AMA Style**

Hirsch-Eshkol T, Baharad A, Alpert P.
Investigation of the Dominant Factors Influencing the ERA15 Temperature Increments at the Subtropical and Temperate Belts with a Focus over the Eastern Mediterranean Region. *Land*. 2014; 3(3):1015-1036.
https://doi.org/10.3390/land3031015

**Chicago/Turabian Style**

Hirsch-Eshkol, Tali, Anat Baharad, and Pinhas Alpert.
2014. "Investigation of the Dominant Factors Influencing the ERA15 Temperature Increments at the Subtropical and Temperate Belts with a Focus over the Eastern Mediterranean Region" *Land* 3, no. 3: 1015-1036.
https://doi.org/10.3390/land3031015