Global Temperature Analysis

Proposal

Introduction

NASA has announced that 2023 was the warmest year on record, as detailed in their recent announcement. Utilizing global surface temperature data, including the latest updates for 2023 from the NASA GISS Surface Temperature Analysis (GISTEMP v4), we aim to validate this claim through statistical analysis and comparison against historical temperature records. This endeavor not only highlights the ongoing trends in global warming but also underscores the critical importance of addressing climate change.

Dataset

The dataset is provided by NASA by combining land-surface, sea surface, and air temperature anomalies. The values in the dataset are deviations from the corresponding 1951-1980 means, indicating how much warmer or colder it is than normal for a particular place and time, and the normal means the average over the 30 years 1951-1980 for that place and time of year. More precisely, the base 1951-1980 mean is subtracted from the absolute temperature to get the anomaly. The dataset contains four CSV files, providing temperature anomalies across different spatial and temporal scales for a long period from 1880 to 2022. Below you can find the datasets and a brief description of the variables.

Northern Hemisphere Annomalies and Description

	Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	J-D	D-N	DJF	MAM	JJA	SON
0	1880	-0.37	-0.52	-0.24	-0.31	-0.07	-0.17	-0.19	-0.27	-0.24	-0.33	-0.44	-0.41	-0.30	NaN	NaN	-0.20	-0.21	-0.34
1	1881	-0.32	-0.24	-0.05	-0.01	0.02	-0.34	0.07	-0.05	-0.27	-0.45	-0.38	-0.25	-0.19	-0.20	-0.32	-0.01	-0.11	-0.37
2	1882	0.24	0.20	0.01	-0.31	-0.24	-0.29	-0.29	-0.17	-0.27	-0.53	-0.35	-0.70	-0.22	-0.19	0.06	-0.18	-0.25	-0.38
3	1883	-0.59	-0.67	-0.16	-0.31	-0.26	-0.12	-0.06	-0.23	-0.35	-0.17	-0.45	-0.16	-0.30	-0.34	-0.65	-0.24	-0.14	-0.33
4	1884	-0.18	-0.12	-0.65	-0.60	-0.37	-0.42	-0.42	-0.52	-0.46	-0.45	-0.58	-0.48	-0.44	-0.41	-0.15	-0.54	-0.45	-0.50
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
139	2019	1.20	1.12	1.55	1.25	0.98	1.19	1.03	1.09	1.21	1.30	1.20	1.39	1.21	1.18	1.14	1.26	1.10	1.24
140	2020	1.59	1.71	1.66	1.40	1.28	1.13	1.10	1.13	1.20	1.21	1.61	1.23	1.35	1.37	1.56	1.45	1.12	1.34
141	2021	1.26	0.96	1.20	1.13	1.04	1.21	1.07	1.03	1.06	1.31	1.31	1.14	1.14	1.15	1.15	1.12	1.10	1.23
142	2022	1.25	1.17	1.43	1.09	1.01	1.13	1.05	1.17	1.16	1.32	1.10	1.07	1.16	1.17	1.19	1.18	1.12	1.19
143	2023	1.29	1.32	1.60	1.02	1.12	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.23	1.25	NaN	NaN

144 rows × 19 columns

|  variable  |  class  |        description         |
|:----------:|:-------:|:--------------------------:|
|    Year    | double  |            Year            |
|    Jan     | double  |          January           |
|    Feb     | double  |          February          |
|    Mar     | double  |           March            |
|    Apr     | double  |           April            |
|    May     | double  |            May             |
|    Jun     | double  |            June            |
|    Jul     | double  |            July            |
|    Aug     | double  |           August           |
|    Sep     | double  |         September          |
|    Oct     | double  |          October           |
|    Nov     | double  |          November          |
|    Dec     | double  |          December          |
|    J-D     | double  |      January-December      |
|    D-N     | double  |     Decemeber-November     |
|    DJF     | double  | December-January-February  |
|    MAM     | double  |      March-April-May       |
|    JJA     | double  |      June-July-August      |
|    SON     | double  | September-October-November |

Southern Hemisphere Annomalies and Description

	Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	J-D	D-N	DJF	MAM	JJA	SON
0	1880	0.00	0.03	0.06	-0.02	-0.13	-0.25	-0.17	0.06	-0.05	-0.15	0.00	0.05	-0.05	NaN	NaN	-0.03	-0.12	-0.07
1	1881	-0.09	-0.06	0.09	0.10	0.08	-0.06	-0.08	-0.03	-0.05	-0.01	0.00	0.09	0.00	0.00	-0.03	0.09	-0.05	-0.02
2	1882	0.07	0.07	0.07	-0.02	-0.04	-0.15	-0.05	0.01	-0.05	0.03	-0.01	-0.07	-0.01	0.00	0.08	0.00	-0.06	-0.01
3	1883	-0.02	-0.08	-0.09	-0.07	-0.10	-0.02	-0.09	-0.05	-0.11	-0.06	-0.04	-0.06	-0.07	-0.07	-0.06	-0.09	-0.05	-0.07
4	1884	-0.09	-0.06	-0.10	-0.22	-0.32	-0.30	-0.22	-0.07	-0.11	-0.07	-0.11	-0.15	-0.15	-0.14	-0.07	-0.21	-0.19	-0.10
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
139	2019	0.67	0.78	0.79	0.79	0.73	0.63	0.86	0.81	0.65	0.73	0.80	0.79	0.75	0.75	0.73	0.77	0.77	0.73
140	2020	0.76	0.78	0.68	0.86	0.76	0.72	0.70	0.64	0.78	0.57	0.60	0.40	0.69	0.72	0.78	0.76	0.68	0.65
141	2021	0.36	0.32	0.57	0.39	0.54	0.49	0.77	0.62	0.80	0.68	0.57	0.57	0.56	0.54	0.36	0.50	0.63	0.68
142	2022	0.57	0.61	0.67	0.59	0.67	0.72	0.81	0.74	0.64	0.62	0.36	0.53	0.63	0.63	0.58	0.64	0.76	0.54
143	2023	0.44	0.63	0.80	0.98	0.77	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.53	0.85	NaN	NaN

144 rows × 19 columns

|  variable  |  class  |        description         |
|:----------:|:-------:|:--------------------------:|
|    Year    | double  |            Year            |
|    Jan     | double  |          January           |
|    Feb     | double  |          February          |
|    Mar     | double  |           March            |
|    Apr     | double  |           April            |
|    May     | double  |            May             |
|    Jun     | double  |            June            |
|    Jul     | double  |            July            |
|    Aug     | double  |           August           |
|    Sep     | double  |         September          |
|    Oct     | double  |          October           |
|    Nov     | double  |          November          |
|    Dec     | double  |          December          |
|    J-D     | double  |      January-December      |
|    D-N     | double  |     Decemeber-November     |
|    DJF     | double  | December-January-February  |
|    MAM     | double  |      March-April-May       |
|    JJA     | double  |      June-July-August      |
|    SON     | double  | September-October-November |

Zonnal Annomalies and Description

	Year	Glob	NHem	SHem	24N-90N	24S-24N	90S-24S	64N-90N	44N-64N	24N-44N	EQU-24N	24S-EQU	44S-24S	64S-44S	90S-64S
0	1880	-0.17	-0.30	-0.05	-0.39	-0.13	-0.01	-0.82	-0.50	-0.30	-0.15	-0.11	-0.04	0.05	0.66
1	1881	-0.09	-0.19	0.00	-0.37	0.10	-0.07	-0.92	-0.47	-0.23	0.10	0.10	-0.05	-0.07	0.59
2	1882	-0.11	-0.22	-0.01	-0.33	-0.05	0.01	-1.41	-0.28	-0.18	-0.05	-0.05	0.01	0.04	0.62
3	1883	-0.18	-0.30	-0.07	-0.37	-0.17	-0.01	-0.19	-0.57	-0.29	-0.18	-0.16	-0.04	0.07	0.49
4	1884	-0.29	-0.44	-0.15	-0.62	-0.15	-0.14	-1.31	-0.65	-0.49	-0.12	-0.17	-0.19	-0.02	0.64
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
138	2018	0.85	1.03	0.67	1.24	0.68	0.69	2.13	1.09	1.05	0.73	0.63	0.80	0.37	0.97
139	2019	0.98	1.21	0.75	1.42	0.90	0.65	2.70	1.43	0.99	0.91	0.89	0.75	0.39	0.86
140	2020	1.02	1.35	0.69	1.67	0.86	0.58	2.90	1.82	1.19	0.89	0.84	0.59	0.39	0.92
141	2021	0.85	1.14	0.56	1.42	0.66	0.53	2.04	1.36	1.26	0.73	0.59	0.73	0.32	0.31
142	2022	0.90	1.16	0.63	1.52	0.57	0.71	2.33	1.51	1.27	0.63	0.51	0.79	0.39	1.12

143 rows × 15 columns

|  variable  |  class  |     description     |
|:----------:|:-------:|:-------------------:|
|    Year    | double  |        Year         |
|    Glob    | double  |       Global        |
|    NHem    | double  | Northern Hemisphere |
|    SHem    | double  | Southern Hemisphere |
|  24N-90N   | double  |  24N-90N lattitude  |
|  24S-24N   | double  |  24S-24N lattitude  |
|  90S-24S   | double  |  90S-24S lattitude  |
|  64N-90N   | double  |  64N-90N lattitude  |
|  44N-64N   | double  |  44N-64N lattitude  |
|  24N-44N   | double  |  24N-44N lattitude  |
|  EQU-24N   | double  |  EQU-24N lattitude  |
|  24S-EQU   | double  |  24S-EQU lattitude  |
|  44S-24S   | double  |  44S-24S lattitude  |
|  64S-44S   | double  |  64S-44S lattitude  |
|  90S-64S   | double  |  90S-64S lattitude  |

Questions

How have zonal annual temperatures changed over the years? Can we detect any latitudinal pattern? When was the hottest year on record?
How have the hemispheric seasonal (monthly) temperatures changed over the years? Can we detect any upward or downward trend in any seasons (months) and any of the hemispheres? Which seasons (months) show more erratic behavior?

Analysis plan

Question one

Variables Involved

Yearly Temperature Anomalies: Utilizing the J-D (January-December average) column from global_temps.csv, nh_temps.csv, and sh_temps.csv datasets to assess annual temperature anomalies globally and across hemispheres.
Seasonal Temperature Anomalies: Focusing on DJF (December-January-February) and JJA (June-July-August) anomalies for the cool and warm seasons, respectively, in the Northern Hemisphere, with inverted seasons for the Southern Hemisphere.

Variables to be Created

2023 Temperature Anomalies: Adding complete data for 2023 from the GISS Surface Temperature Analysis (GISTEMP v4) to the TidyTuesday datasets.
Seasonal Anomalies for 2023: Calculating mean temperature anomalies for the cool (November-April) and warm (May-October) seasons for 2023.

External Data

2023 Temperature Data: Sourcing the missing 2023 temperature anomaly data from NASA’s GISS Surface Temperature Analysis (NASA Goddard Institute for Space Studies).

Statistical Methods

Time Series Analysis: To compare 2023 temperature anomalies against historical records.
Trend Analysis: Employing time series analysis to assess temperature anomaly trends over the years, including 2023.
Comparative Analysis: Comparing 2023 average temperature anomalies against historical maxima for global and hemispheric analyses, and evaluating cool and warm season anomalies.

We should use variables inside the zonnan_temps.csv dataset. Initially, we should transform the data from a wide format to a long format by defining a new column called zones and merging all eight variables related to zones (i.e. 24N-44N, …) to that column. As we are focusing on annual zonal anomalies in this qustion, we can skip Glob, NHem, and SHem variables. Finally, we would have a dataframe with three columns with one represnting year, one represnting zone and the last contains the annomalies. We can create time series for each zone to visualize the pattern of zonal annual temperature anomalies, emphasizing any noticeable trends or deviation from zonal long-term mean anomaly. Also, we can employ heatmaps to map the variance of the zonal annual temperature anomaly on top of countries and oceans boundaries to show which hemisphere behaves more erratically.
We should use nh_temps.csv and sh_temps.csv datasets. We should start by converting the dataset of each hemispheres to a long format by defining one new column for month (season) and one column that shows the hemispheres (NH and SH). Finally, we can merge the dataframes vertically to arrive at a one comprehensive dataframe. We can compare the pattern of seasonal temperature anomalies over the Northern and Southern hemispheres separately and collectively. We can use time series, box plots, or histograms to illustrate and compare the time series and statistical distribution of seasonal temperature anomalies in the northern and southern hemispheres to discuss how different and similar they are. It may be interesting to create a similar visualization to the previous question with deviation from long-term anomalies. This would not only capture how seasonal temperatures have strayed from some long-term average, but how temperatures are fluctuating relative to even temporally local averages