= Solution
The selected model is a <moving-average process of order one>, with the plus-sign convention used by R:
$$
\boxed{Y_t=\mu+\varepsilon_t+\theta\varepsilon_{t-1},\quad \widehat\mu=15.0133,\quad \widehat\theta=0.6727,\quad \widehat\sigma_\varepsilon^2=4.634.}
$$
The Gaussian <likelihood function> models innovations as independent $N(0,\sigma_\varepsilon^2)$. Its <autocovariance> is $\gamma(0)=\sigma_\varepsilon^2(1+\theta^2)$, $\gamma(1)=\sigma_\varepsilon^2\theta$, and zero at larger lags, with <autocorrelation> $\rho(1)=\theta/(1+\theta^2)\simeq0.4631$. The estimated coefficient has modulus less than one, giving <invertibility of a moving-average model>; a finite moving-average process is stationary regardless of this invertibility restriction.
The original plots show fluctuations around a roughly constant level, no persuasive deterministic trend, and a conspicuous positive lag-one correlation with the other plotted correlations within the approximate sampling bands. They make a stationary nonseasonal working model reasonable for this short series. `stationary=TRUE` excludes differenced alternatives, and `seasonal=FALSE` excludes seasonal ARIMA terms. Neither option is proved by a 60-day plot: annual temperature seasonality cannot be excluded from two months, and a longer record or residual diagnostics might require a different model. The options are reasonable local simplifications, not universal statements about temperature dynamics.
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